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Proceedings of the HIMAA/NCCH 36th National Conference

Health Information Management: Celebrating 70 years

of Strength in Diversity

23–25 October 2019 Bankwest Stadium, Parramatta

Proceedings of the HIMAA NCCH 36th National Conference


Proceedings of the HIMAA/NCCH 36th National Conference, Health Information Management: Engaging the Next Generation. 23 - 25 October 2019, Western Sydney Stadium, Parramatta, New South Wales, Australia

Copyright 2019 Health Information Management Association of Australia Ltd/National Centre for Classification in Health, The University of Sydney

Papers published in these proceedings have been fully peer-reviewed by independent, qualified referees prior to publication.

Editor(s): Kerryn Butler-Henderson, College of Health and Medicine University of Tasmania Chair, Academic Panel

Linda Westbrook, The Coding Company Conference Chair

Vera Dimitropoulos, National Centre for Classification in Health, The University of Sydney Conference Deputy Chair

Publisher: Health Information Management Association of Australia Ltd Locked Bag 2045 North Ryde NSW 1670

National Centre for Classification in Health The University of Sydney PO Box 170 Lidcombe NSW 1825

ISSN 978-0-9946206-6-8 Proceedings of the HIMAA NCCH 2019 National Conference, Health Information Management: Celebrating 70 years of Strength in Diversity


Members of HIMAA/NCCH Conference Committee for 2019 Kerryn Butler-Henderson (Chair, Academic Panel), University of Tasmania

Linda Westbrook (Conference Chair), The Coding Company

Vera Dimitropoulos (Conference Deputy Chair), NCCH

Grant Duffill, West Moreton Health/HIMAA

Dana Higgins, NCCH

Trixie Kemp, Tasmanian Health Service

Milla Krivozhnya, HIMAA

Richard Lawrance, HIMAA

Nina Lean, Western Sydney Local Health District

Imelda Noti, NCCH

Lyn Williams, HIMAA

Academic Panel members Dr Kerryn Butler-Henderson (Chair of Academic Panel) University of Tasmania

Ms Vicki Bennett, Australian Institute of Health and Welfare

Ms Sharon Campbell, La Trobe University

Dr Anupama Ginige, Western Sydney University

Ms Trixie Kemp, Tasmanian Health Service

Mr Gavin Lackey, St Vincent Health Sydney

Dr Mary Lam, University of Technology Sydney

Ms Nina Lean, Western Sydney Local Health District

Ms Sheree Lloyd, Griffiths University

Ms Sarah Low, University of Tasmania

Ms Miriam Lum On, Australian Institute of Health and Welfare

Ms Tanija Tarabay, Queensland Health

Ms Linda Westbrook, The Coding Company

Ms Maryann Wood, Queensland University of Technology


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Proceedings of the HIMAA NCCH 36th National Conference i

Contents

Peer-reviewed papers THREE KEY FACTORS INFLUENCING HIMAA MEMBERSHIP - GEOGRAPHICAL LOCATION, COUNTRY OF BIRTH AND QUALIFICATION 2

Eleni Gilden, Mary K Lam, Tony Kalathil Jose, Wasanthi Dharmadasa

USING CHADX TO IDENTIFY ADVERSE EVENTS IN A SMALL HOSPITAL SETTING12

Kaye Borgelt

HEALTH INFORMATION MANAGEMENT IN AUSTRALIA: FINDINGS FROM THE 2018 AUSTRALIAN HIW CENSUS 22

Kerryn Butler-Henderson, Kathleen Gray, Ann Ritchie, Angela Ryan, Julie Brophy, Christopher Pearce, Louise K Schaper, Vicki Bennett

INTRODUCING STATISTICAL SAMPLING AND QUANTITATIVE ANALYSIS TO THE AUDIT OF CLINICAL CODING 29

Paraic Bergin, Carolyn Madigan, Riette Joubert

DISTRIBUTED KNOWLEDGE-BASED COMPUTER-ASSISTED CLINICAL CODING SYSTEM 43

Rajvir Kaur, Jeewani Anupama Ginige

DEVELOPMENT OF AN INTERACTIVE MESSENGER CHATBOT FOR MEDICATION AND HEALTH SUPPLEMENT REMINDERS 51

Kerry Y. Fang Heidi Bjering

Professional practice abstracts MAINTAINING DATA QUALITY IN A LARGE PATIENT ADMINISTRATION SYSTEM, WHAT'S IT WORTH? 62

Karen Baker, Joy Smith

DISCOVERING THE POTENTIAL OF AUSTRALIA'S FIRST LIFELONG PERSON-CENTRIC HEALTH DATA SET 65

Vicki Bennett, Navreet Bhattal, Jaclyn Chan

WHERE ON EARTH IS TUVALU? MY YEAR AS A VOLUNTEER HIM IN A DEVELOPING COUNTRY 67

Kaye Borgelt

“SHARING IS CARING” – A REGIONAL EMR 69

Claire Bridson

Proceedings of the HIMAA NCCH 36th National Conference ii

RELATIVE STAY INDEX VERSION 2.0 – AN IMPROVED MEASURE OF LENGTH OF STAY. 71

Rohan Cattell, Alix Higgins

IS THE HEALTH INFORMATION PROFESSION AN AGING WORKFORCE: INSIGHTS FROM AUSTRALIA’S FIRST NATIONAL WORKFORCE CENSUS 73

Kristina Donovan, Kerryn Butler-Henderson

CLINICAL DOCUMENTATION IMPROVEMENT PROGRAMS: MEASURING PERFORMANCE 76

Nicole Draper

FAMINE TO FEAST – BUILDING, DEVELOPING AND RETAINING A CLINICAL CODING WORKFORCE 78

Grant Duffill, Meleah Herbert

IT’S ALL ABOUT DATA! 81

Christine Fan

DEVELOPMENT OF AN INDICATOR REGISTRY FOR THE HEALTH ROUNDTABLE83

Alix Higgins

WHY REVIEW YOUR INTERNAL CLINICAL CODING TRAINING PROGRAM 85

Anthea Ho

AUSTRALIAN EMERGENCY CARE CLASSIFICATION (AECC) VERSION 1.0 87

James Katte

SNAKE PIT TO TEAM OF CHOICE 89

Trixie Kemp

WE’VE HAD A DATA BREACH – NOW WHAT? 92

Kirstie Mountain

THE EVOLVING ROLE OF THE CLINICAL CODER IN IRELAND. 95

Deirdre Murphy, Maureen Lynn, Jacqueline Curley, Marie Glynn

NATURAL LANGUAGE PROCESSING 101 – UNDERSTANDING NLP AND APPLICABLE USES IN HEALTHCARE 97

Laura Pietromica

CLINICAL DOCUMENTATION SPECIALIST (CDS): THE ROLE, ITS IMPLEMENTATION AND PATH TO SUCCESS. 99

Joanne Pitsaris

ESTABLISHMENT AND SUCCESSFUL IMPLEMENTATION OF A CODING FROM HOME PROGRAM 101

Kara Pollard

Proceedings of the HIMAA NCCH 36th National Conference iii

FACTORS ASSOCIATED WITH CODING QUALITY: COMPARISON OF STROKE REGISTRY AND ADMINISTRATIVE DATA 103

Olivia Ryan, Merilyn Riley, Sibilah Breen, Kate Paice, Sam Shehata, Natasha Lannin, Dominique Cadilhac, Monique Kilkenny

CAPTURING THE EXPERIENCES OF SURVIVORS OF TORTURE AND TRAUMA 105

Carlena Phi Phuong Tu

Posters

IMPROVING PATIENT ALERT DATA QUALITY IN MATER'S PATIENT ADMINISTRATION SYSTEM (PAS) 109

Joy Smith

Workshops

MAKING LEAN SIX SIGMA WORK FOR YOU 111

Paraic Bergin

#METOO IN HIM: UNDERSTANDING SEXUAL VIOLENCE AND THE ETHICAL BYSTANDER 114

Kerryn Butler-Henderson, Richard Lawrance

BENEFITS REALISATION PLANNING FOR DIGITAL HEALTH PROJECTS 116

Alicia Cook

AR-DRGS: WHAT'S NEW IN VERSION 10.0 118

Anne Elsworthy, Sean Heng, Ning Ding, Heon Lee

INCORPORATING RESEARCH INTO YOUR EVERYDAY ROLE 120

Jeewani Anupama Ginige, Mary Lam, Kerryn Butler-Henderson, Vicki Bennett, Tanija Tarabay, Gavin Lackey, Gowri Sriraman

Proceedings of the HIMAA NCCH 36th National Conference iv

Preface Health Information Management: Celebrating 70 years of Strength in Diversity

The professions of health information management and clinical classification has come a long way over the past 70 years since the establishment of the New South Wales Association of Medical Records Librarians and the Victorian Association of Medical Librarians in 1949. From the days of Medical Record Librarians to today’s information governance and management experts, the Health Information Management Professionals have always provided leadership in the organisation of health data and information across the sector and around the country.

Today’s Health Information Management Professionals are diverse in their skills and capabilities, continuing to be the heart of the healthcare system. Therefore, this year’s conference was aptly titled:

Health Information Management: Celebrating 70 years of Strength in Diversity.

This year’s program highlights the research and practice solutions that demonstrate the strength in diversity in the field of health information.

The HIMAA NCCH national conference is an exhibition of leading research and practice in the field of health information, across the themes of clinical classifications, clinical documentation improvement, data and data quality, digital health, quality and safety, workforce, and, this year, diversity in the Health Information Management profession.

Associate Professor Kerryn Butler-Henderson, Chair, Academic Panel

Linda Westbrook Conference Chair

Vera Dimitropoulos Conference Deputy Chair

Proceedings of the HIMAA NCCH 36th National Conference v


Proceedings of the HIMAA NCCH 36th National Conference 1

Peer-reviewed papers


Three key factors influencing HIMAA membership - Geographical location, country of birth and qualification Eleni Gilden1, Mary K Lam2, Tony Kalathil Jose3, Wasanthi Dharmadasa4 1Epworth Healthcare, Southbank, Australia, 2University of Technology Sydney, Sydney, Australia, 3Womens and Newborn Health Service, Subiaco, Australia, 4Top End Health Service, Darwin, Australia

Abstract

Introduction: The current workforce in health information is changing due to advances in e-Health and funding models. A Health Information Workforce Census conducted in 2018 investigated the current HIM workforce. However, minimal research has been performed in exploring factors that influence membership of Health Information Management Association Australia (HIMAA). Objective: The aim of this paper is to examine the current workforce of Health Information Managers (HIMs) and Clinical Coders (CCs) and determine the factors that influence persons to join HIMAA.

Method: This paper examined data items from the 2018 census provided by the data custodian. The total number of respondents was 641 and the analysis was performed using a statistical analysis software system (IBM SPSS Statistics Version 25) for data manipulation and analysis. Descriptive analyses were applied with means and standard deviations for continuous variables and counts and percentages for categorical variables. Chi-square test was used to determine the association between categorical variables, while two sample T-test was used to examine the relationship between continuous and categorical variables.

Results: Of the 641 respondents; 67.4% identified as HIMs and 32.6% as CCs. Of the five variables of interest (age, gender, country of birth, state and highest qualification), only two were significantly associated with professional membership, namely state of residence (p=0.025) and highest qualification (p=0.016). Further analysis revealed an interaction between country of birth and highest qualification in HIMAA members (p<0.001).

Discussion: Results reinforced a female dominated industry and the mean age of 45 years which is consistent with other reports. Most members come from the Eastern States with Queensland having the highest proportion of HIMAA members compared to non-members. Additionally, HIMAA members born overseas had a higher proportion of post-graduate qualifications compared to members born in Australia.

Conclusion: Three key factors were found to influence uptake of HIMAA membership: geographical location, country of birth and qualification. There is limited knowledge in this area and further research could investigate these factors further. The results can be used by HIMAA to develop strategies to attract more people to the profession and further strengthen HIMAA.


1. Introduction

The workforce involved in health information is constantly changing and will continue to do so as we move from traditional roles that were formerly dependent on paper and print to platforms of eHealth (Gaddi, Capello, & Manca 2014). This change will see roles increase in the fields of data management, security and privacy officers and workflow analysts (Butler-Henderson 2017).

The professional body, the Health Information Management Association of Australia (HIMAA), is celebrating 70 years in 2019. It is important and timely to understand our current workforce and to determine whether any trends exist, particularly factors that influence the likelihood of a person taking up affiliation with their professional body. Membership of a professional association is important for many health professionals as it provides opportunities for professional development, ways to share knowledge and a sense of belonging and recognition by peers, all of which HIMAA has been providing for the past seventy years (Markova et al. 2013).

As a professional body depends on its members, encouraging Health Information Managers (HIMs) and Clinical Coders (CCs) of different ages, ethnicities, level of qualification, experience and residence in terms of state will bring diversity to the professional body and will contribute to strengthening the organisation as the profession moves forward together in navigating the changing nature of our work. The 2018 Health Information Workforce Census (HIWC) (Butler-Henderson et al. 2017), in addition to other surveys completed in 2009 and 2011, investigated the current workforce and provided an overview of the industry. However, very little research has focused on factors that determine whether a person will join the professional body of HIMAA. Therefore, the objective of this paper is to examine the current HIM and CC workforce in terms of demographics such as geographical location, country of birth and educational qualification, and to determine whether any of these factors influence a person in joining the professional body of HIMAA.

Our investigations were conducted in a staged approach in the form of two studies. The first part of this study looked at a subset of the data collected by the 2018 Australian HIWC (Butler-Henderson et al. 2017) to determine what factors influence persons to join the professional body. In addition, we explored whether there was a relationship between educational attainment and country of birth, as this information may be prudent in our understanding of what types of qualification are obtained. The second paper will look at text descriptions provided by respondents about their health information occupation and will examine emerging occupations in the current workforce.

The results from this study can be used to understand what factors influence persons in taking up HIMAA membership. An understanding of these factors can help HIMAA to develop strategies that attract more people to join the professional body, and it can also be used over time to track the profession and determine changes in these variables in years to come.


2. Methods

2.1 Dataset and ethics

Data items of the 2018 HIWC provided by the data custodian were analysed (Butler-Henderson et al. 2017). Ethics approval was obtained from the University of Technology Sydney Human Research Ethics Committee (ETH18-3099). The data was received and imported into a statistical analysis software system (IBM SPSS Statistics Version 25) for data manipulation and analysis.

2.2 Data management

To address the research aim, four new variables were created using existing data items. First, a HIMAA membership variable was created from the 21 multiple response data item on a participant’s membership status of a known list of professional associations such as HIMAA, Health Informatics Society of Australia (HISA), Healthcare Financial Management Association (HFMA), and so on. The new variable grouped participants into two categories: No/Others (those who identified themselves as “not currently a member of any professional organisation”, and those who identified themselves as member of any organisations other than HIMAA), and HIMAA member. Secondly, due to the small number of counts in overseas country of birth, the current ‘country of birth’ variable was regrouped into a new variable with only two categories: those who were born in Australia and those who were born overseas. Thirdly, a new variable of estimated age was created, using the difference between 2018 and the information provided on year of birth.

A fourth variable was created to identify the highest qualification obtained by participants. This was created by combining quantitative responses provided by participants in answer to the question on “What is highest qualification?” or another question on the detailed education background. This variable grouped participants into four educational categories: pre-undergraduate (Certificate 1-IV, Diploma, Associate degree, VET qualification), undergraduate (undergraduate/Bachelor degree, Bachelor honours degree), postgraduate (Graduate certificate or diploma, postgraduate/Masters, doctorate), and other/none.

2.3 Data analysis

Descriptive statistics, mean and standard deviation (for continuous variables), together with count and percentage (for categorical variables), were used to provide a general description of the variables of interest. Chi-square analysis was used to examine the association between the variable HIMAA membership and categorical variables (such as highest qualification obtained), while independent sample T test was used to examine the relationship between HIMAA membership and estimated age.

3. Results

The data of 641 participants was extracted and provided by the data custodian based on the information participants provided for the question on health information occupation category. Of the 641 participants, 209 (32.6%) identified as CCs in comparison to 432 (67.4%) who classified themselves as HIMs. Table 1 provides a summary of the relationship between HIMAA membership and the demographic variables of interest.


Table 1. Relationship between age, gender, country of birth and state in relation to HIMAA membership

Professional Membership No/Others HIMAA

Test statistics P value N=326 N=315

Mean (SD) Mean (SD)

Age (Estimated) 45.1 (+/-11.6)

45.3 (+/-11.2) T639 = 0.17 0.868

N (%) N (%)

Gender

χ2(1) = 0.82 0.365 Male 37 (11.4%) 29 (9.2%)

Female 288 (88.6%) 286 (90.8%)

Country of birth

χ2(1) = 1.01 0.316 Australia 264 (81%) 245 (77.8%)

Overseas 62 (19%) 70 (22.2%)

State

χ2(7) = 16.0 0.025

ACT <5 (0.9%) 10 (3.2%)

NSW 75 (23.1%) 67 (21.5%)

VIC 143 (44.1%) 121 (38.8%)

QLD 41 (12.7%) 62 (19.9%)

SA 18 (5.6%) 13 (4.2%)

WA 35 (10.8%) 24 (7.7%)

TAS <5 (1.5%) 12 (3.8%)

NT <5 (1.2%) <5 (1%)

Of the four demographic variables of interest, only State of Residence was found to be significantly associated with professional membership (χ2(7) = 16.0, p=0.025). Victoria (121) had the highest number of HIMAA members, followed by New South Wales (67), Queensland (62) and Western Australia (24); states with fewer member numbers were Northern Territory (<5), Australian Capital Territory (10), Tasmania (12), and South Australia (13).

Within the States and Territories, the highest proportion of HIMAA members were in Australian Capital Territory (77%), Tasmania (71%), Queensland (60%), New South Wales (47%) and Victoria (46%). The proportion of HIMAA membership is smaller in Western Australia (41%), South Australia (42%) and Northern Territory (43%). A further analysis was conducted for the three states with the highest number of respondents, namely NSW, Victoria and Queensland. A marginally significant association between HIMAA membership and State of Residence was found,


with slightly more HIMAA members than no/Other members coming from Queensland (χ2(2) = 6.41, p=0.041) (Table 2).

Table 2. Relationship between HIMAA membership and the States of NSW, VIC and QLD

Professional Membership

No/Others HIMAA Test statistics P value

State N=326 N=315

χ2(2) = 6.41 0.041

NSW 75 (29.0%) 67 (26.8%)

VIC 143 (55.2%) 121 (48.4%)

QLD 41 (15.8%) 62 (24.8%)

Table 3. Relationship between HIMAA membership and qualifications

Professional Membership

No/Others HIMAA Test Statistics P Value

Highest qualification N=322 N=312

χ2(3) = 10.4 0.016

Pre-Undergraduate 43 (13.4%) 35 (11.2%)

Bachelor 154 (47.8%) 163 (52.2%)

Postgraduate 79 (24.5%) 92 (29.5%)

Others/None 46 (14.3%) 22 (7.1%)

Educational qualification was also significantly associated with HIMAA membership (χ2(3) = 10.4, p=0.016). HIMAA members tended to have a higher proportion of bachelor degrees (52.2% vs 47.8%) and postgraduate qualifications (29.5% vs 24.5%) in comparison to members of other professional bodies or who held no membership at all.


Table 4. Relationship between country of birth and highest qualification for HIMAA members and non-members.

Professional Membership No/Others HIMAA

Australia Overseas Test statistics Australia Overseas Test statistics

Highest qualification N=223 N=53

χ2(2) = 0.79

p=0.674

N=224 N=66

χ2(2) = 16.54

p<0.001

Pre-UG 33 (14.8%)

10 (18.9%)

27 (12.1%) 8 (12.1%)

Bachelor 127 (57%)

27 (50.9%)

139 (62.1%) 24 (36.4%)

Postgraduate 63 (28.3%)

16 (30.2%)

58 (25.9%) 34 (51.5%)

Further analysis was conducted to test any association between the highest qualification and country of birth among HIMAA members, and non-members or members of other professional bodies (No/Others group). The results suggested that there was no significant association between these variables for No/Others group. However, a significant relationship was found for HIMAA members (χ22 = 16.54, p<0.001); which showed that HIMAA members born overseas have a greater proportion of postgraduate qualifications in comparison to members born in Australia (51.5% vs 25.9%).

4. Discussion

4.1 Demographic results

In terms of demographics, there was no significant association found between gender and age in relation to HIMAA membership. When comparing the demographic results of age and gender to previous studies, there was very little research available to make a comparison, as no other study has examined the demographics of the current workforce in relation to uptake of membership. Therefore, our results were compared with studies that looked at the current coding workforce made up of HIMs and CCs, which showed consistent results. In the current workforce, there is a higher proportion of females (89.7%) in the profession compared to males (10.3%), which is supported by other studies that show the industry to be female dominated (Australian Institute of Health and Welfare (AIHW) 2010; Callen & Craig 2000). In terms of HIMAA membership, there was a greater proportion of female members (90.8%) compared to males (9.2%) which is expected, given that the majority of professionals in the workforce are female. The mean age for both HIMAA members and non-HIMAA members is approximately 45 years old. This result is consistent with the 2010 Coding workforce shortfall report, which noted that a large proportion of the coding workforce is over the age of 45 (AIHW 2010). A recent survey undertaken by HIMAA in


2016 also supports the result of an ageing workforce. The survey found that most of its members fall into the age group category of 40-65. This may suggest that either a majority of members are choosing to stay longer in the profession, or, it is their second career choice (Health Workforce Australia (HWA) 2013; Lawrance 2016).

4.2 Highest qualification of HIMAA membership

Educational qualification was also significantly associated with HIMAA membership. Among HIMAA members, there is a higher proportion of bachelor and postgraduate degree qualifications compared to those members of other professional bodies, or those without any memberships. However, the higher proportion of undergraduate degrees may be a bias, as the minimum qualification requirement to work as a HIM is to hold a bachelor’s degree. Also, HIMAA’s policy is that the “minimum qualification for course accreditation” (Robinson & Williamson 2016, p. 4) is a bachelor’s degree for full membership.

According to Brittain and Norris (2000), bachelor degrees are the first preference among students in health information education. However, they tend to seek pre-undergraduate qualifications such as diplomas or certificates from an established organisation if the bachelor’s degree requires more work or is expensive. A study completed in NSW found that 59% of HIMs have a bachelor degree, 9% have postgraduate qualifications and 24% pre-undergraduate (Robertson & Callen 2004). Another study compared NSW and Victoria HIM qualifications. The results revealed that 64% of HIMs have an undergraduate or postgraduate qualification, while 28% hold a pre-undergraduate qualification (Callen & Craig 2000). A study among HIM students in professional activities and postgraduate education showed that 62% of students indicated that they would become members of HIMAA, and 60% of students indicated that they would progress their education into postgraduate studies (Westbrook et al. 2000). Even though these past studies do not clearly demonstrate the significance in relationship between qualification and HIMAA membership, one possible reason for the finding is that undergraduate and postgraduate HIM professionals rely on the support extended by HIMAA for professional development.

4.3 Country of birth, highest qualification vs HIMAA membership

HIMAA members born overseas have a greater proportion of postgraduate qualifications in comparison to members born in Australia. Potential reasons to explain this finding could be the upward trend of tertiary education opportunities for overseas students and migrants. A preliminary inspection of the qualitative descriptions provided by overseas born respondents as to the type of bachelor qualifications obtained, revealed that many have degrees in the clinical field of nursing and midwifery, allied health and medical field before migrating to Australia. In order to practice in the field of their original profession, most have to sit a registration exam. Due to various reasons, some may choose to change their career path from practicing as a clinician to an area in health information which requires postgraduate study. The attraction to go down this pathway may also be due to the extensive further study required if they were to continue with their original profession. A study conducted by Westbrook and colleagues (1997) lends support to this explanation. Their study of postgraduate qualifications for HIM showed that 17% of the enrolment was made up of overseas-qualified clinicians (Westbrook et al. 1997). Re-establishing oneself in a new country and a new profession requires a tremendous amount of peer support and networking opportunities. In this regard, professional organisations serve to provide not only a venue for professional networking and peer support but a sense of belonging and recognition for the individual (Markova et al. 2013). These are important contributing factors for immigrants to integrate successfully into


Australian society. For many HIMs who were born overseas and have attained the HIM qualification through postgraduate study, the next logical step is to become a member of the profession, where they embark on a new professional journey. When trying to compare our result with previous studies, we could not find any literature that explored this relationship in depth; therefore, the results of this analysis provides an opportunity for further study in this area.

4.4 State vs HIMAA membership

The highest number of HIMAA members came from states who have made advances in e-health and introduced activity based funding (Callen & Craig 2000; HWA 2013). Victoria (VIC) implemented the Activity Based Funding model in 1993 (Duckett 1995). The rate of technological advances are high in the Eastern States compared to other States and Territories, which may have resulted in more HIM professionals in states like Victoria, New South Wales (NSW), and Queensland (QLD). Although the Australian Capital Territory and Tasmania recorded the highest proportion of HIMAA members, this may be attributable to the low number of HIM professionals recorded. A further analysis was performed to determine any association between the three states of NSW, VIC and QLD in regard to HIMAA membership. These three States were selected as they had more than 100 respondents within the subset data. The proportion of HIMAA members is higher in QLD compared to NSW and VIC, and this result was statistically significant. It is interesting to note that the QLD government committed $11 billion towards state-wide electronic health records (HWA 2013).

4.5 Limitations

One of the main limitations of this study is the lack of previous studies conducted in this area. This study, did not have much baseline information to compare with. Also, the researchers identified the need to have access to all information provided by the respondents in this census to identify the correlation of HIMs and CCs with other similar professionals.

5. Future Work

The results from our research have indicated potential areas for further research. One area is in our understanding of whether a higher qualification allows the health information management profession to conduct their job effectively, and what HIMAA can do to encourage its members to keep currency of their qualification and continue further professional development. A second area of interest is to further examine whether geographical location, such as state and regional remoteness have a bearing on whether a person becomes a HIMAA member. The third area of interest is to consider whether we are promoting our profession in the right way by highlighting the different careers that one can have as a HIM, whether in data management, research or traditional roles. The next stage of this study will look at these three key areas to explore the initiatives and opportunities that HIMAA can undertake to promote the benefits of being a member. The researchers may also work with census developers to add questions enquiring about reasons of becoming HIMAA members in future census.


6. Conclusion

This paper has examined the current workforce in terms of demographics: geographical location, country of birth and educational qualification to determine whether there are any factors that influence a person joining HIMAA. From this study, three key factors were found to directly influence whether a person becomes a HIMAA member: Geographical location in terms of state, country of birth and qualification. Gender and age had no significant impact. These results highlight areas for further research, the results of which can be used by HIMAA to develop and implement strategies to attract more people to the profession and further strengthen HIMAA in the years to come.

Acknowledgment

Sincere thanks to Frances Guinness, Librarian of Orange Health Service, Orange, NSW for her excellent proof reading and comments of this research paper.

7. References

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Brittain, JM & Norris, AC 2000, ‘Delivery of health informatics education and training.’ Health Libraries Review, vol. 17, no. 3, pp. 117-128.

Butler-Henderson, K 2017, Health Information Management 2025. What is required to create a sustainable profession in the face of digital transformation? University of Tasmania, Launceston, Australia.

Butler-Henderson, K, Gray, K, Green field, D, Low, S, Gilbert, C, Ritchie, A, Trujillo, M, Bennett, V, Brophy, J & Schaper, LK 2017, ‘The development of a national census of the health information workforce: expert panel recommendations’, Stud Health Technol Inform, vol. 239, pp. 8-13.

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Duckett, SJ 1995, 'Hospital payment arrangements to encourage efficiency: the case of Victoria, Australia', Elsevier, vol. 34, no.2, pp. 113-134.

Gaddi, A, Capello, F & Manca, M 2014, Ehealth, care and quality of life, Springer, Milan, viewed 28 April 2019, < DOI:10.1007/978-88-470-5253-6>.

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Lawrance, R 2016, From the office of the CEO, HIMAA Matters, vol. 2, no. 7, pp. 2-3, viewed 27 May 2019, <http://himaa2.org.au/sites/default/files/HIMAA%20Matters%20-%20August%202016_0.pdf>.


Markova, G, Ford, RC, Dickson, DR & Bohn TH 2013, Professional association and members’ benefits: What’s in it for me?, Nonprofit Management and Leadership, vol. 23, no. 4, pp. 491-510, viewed 28 April 2019, <https://doi.org/10.1002/nml.21076>.

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Using CHADx to Identify Adverse Events in a Small Hospital Setting Kaye Borgelt1

1Australian Volunteers International, Netherby, Australia

Abstract

The Classification of Hospital-Acquired Diagnoses (CHADx) tool is an effective method of identifying and monitoring adverse events and hospital-acquired conditions for acute inpatients in large and small datasets. In one small rural public health service referred to as Hospital X, 6.72% of all admissions over a five-year period recorded at least one CHADx with 97.75% of all CHADx episodes occurring in multi-day stay patients. There was a correlation between the types of hospital-acquired conditions and adverse events at a local level when compared to the state-wide data set. CHADx was also an effective means of identifying post-procedural complications and low volume, high acuity complications.

The CHADx tool is an effective method of identifying hospital-acquired conditions and adverse events, and if used alongside incident reporting systems, has the capacity to improve clinical governance processes at a local level, through the identification of serious hospital-acquired complications and also mundane and common complications. Routinely collected data sets are a cost-effective and efficient source of information that can be applied to clinical governance processes at health services large and small.

The objective of the research was to ascertain if the CHADx tool can be used as a local hospital level to identify complications and adverse events, and if the tool is appropriate for use in a small hospital with few, if any, cases in many of the CHADx sub-categories.

1. Introduction:

1.1 Background

Many patients presenting to hospital for treatment will suffer a complication or adverse event that will lead to temporary or permanent disability and sometimes death. Studies in Australia (Ehsani, Jackson, & Duckett, 2006; Wilson et al., 1995), and overseas (Baines et al., 2013; Kohn, Corrigan, & Donaldson, 2000; Vincent, Neale, & Woloshynowych, 2001), have estimated as many as one in ten patients will suffer an adverse event while in hospital, with the Australian experience found to be as high as 16.6% (Wilson et al., 1995). More than half of these events were considered preventable, with 13.7% resulting in permanent disability and 4.9% resulting in death (Wilson et al., 1995). Although more than twenty years has now elapsed since this issue was first raised by Wilson, catastrophic and systematic failures in patient care continue to occur in Australia, including the Queensland Public Hospital Commission of Inquiry which investigated a range of preventable serious adverse events at Bundaberg and Hervey Bay Hospitals (Davis 2005) and a review conducted at Bacchus Marsh Hospital in Victoria, which uncovered at least seven stillbirths in a


two year period that were considered to have been preventable (Australian Commission for Safety and Quality in Health Care, 2015b; Wallace, 2015).

Clinical governance processes in Victoria historically relied heavily on incident reporting, which involves the voluntary reporting of adverse events and near misses (Quality Safety and Patient Experience Branch, 2011). At Bundaberg and Bacchus Marsh Hospitals this method of data collection was found to be unreliable and inconsistent (Australian Commission for Safety and Quality in Health Care, 2015b; Davis 2005).

1.2 Classification of Hospital-Acquired Diagnoses (CHADx)

A tool for identifying and measuring hospital acquired conditions has been developed and endorsed by the Australian Commission on Safety and Quality in Health Care. The system is known as the Classification of Hospital-Acquired Diagnoses (CHADx) and aims to identify and monitor all hospital-acquired diagnoses using routinely collected databases as the major source of information (Jackson, Michel, Roberts, Jorm, & Wakefield, 2009). The CHADx uses a ‘condition onset flag’ to identify hospital-acquired diagnoses and differentiate such diagnoses from those which may have been present on admission. It is also intended “to provide hospitals with a computerised tool to group hospital-acquired diagnoses into a smaller set of clinically meaningful classes for routine monitoring of patient safety and safety improvement efforts” (Jackson et al., 2009, p. 544).

The CHADx tool groups more than 4,500 ICD-10-AM diagnoses into 17 classes and 145 sub-classes that categorise hospital-acquired conditions (Dawson, McNamee, & Navakatikyan, 2014). The tool has been used to interrogate larger health service databases (Cromarty, Parikh, Lim, Acharya, & Jackson, 2014; Trentino, Swain, Burrows, Sprivulis, & Daly, 2013); however, there has been no research analysing how effective the tool is when used within the context of a small database emanating from a small health service, with relatively few discharges per annum and potentially few, if any, cases in many of the CHADx groups.

The Classification of Hospital-Acquired Diagnoses (CHADx) utilises routinely collected morbidity data sets to identify hospital-acquired conditions and adverse events that result in increased care or monitoring, increased length of stay or an unintended outcome of care. Jackson et al. (2009, p. 545) analysed more than 2 million episodes of care in Victoria in 2005-06, grouping 380,833 episodes identified as having at least one hospital-acquired condition. More than 4,000 diagnoses were recognised as being valid hospital-acquired conditions when used with the appropriate condition onset flag. An algorithm was devised to group similar conditions together, maintaining specificity of information within a manageable number of categories. Major CHADx categories are based on body systems and align with the International Classification of Diseases (ICD-10-AM) and specific complication categories, in particular, post procedural complications, adverse drug events and specific infections (Australian Commission for Safety and Quality in Health Care, 2015a).

One of the limitations of using routinely collected datasets to identify and monitor hospital-acquired conditions is the difficulty associated with determining conditions that may be present on admission, and those arising during the course of the inpatient episode (Hauck, Zhao, & Jackson, 2012; Naessens & Huschka, 2004; Verelst et al., 2010; Zhan, Elixhauser, Friedman, Houchens, & Chiang, 2007). The CHADx tool recognises only diagnoses with a complication prefix condition onset flag, with the exception of obstetric and perinatal patients where all diagnoses are mapped (Jackson et al., 2009).


2. Methodology:

2.1 Study Design

The study design selected was a quantitative observational cross-sectional study utilising secondary analysis of routinely collected data at a single site, specifically identifying the prevalence of Classification of Hospital-Acquired Diagnoses (CHADx) within the study population.

The study population comprised all acute and nursing home type discharges from Hospital X for a five (5) year period 2010/11 to 2014/15, a total of 10,567 discharges. The entire dataset was used eliminating the requirement to select a sample from within the broader population.

The 2010-2015 dataset for Hospital X was used as that was the most recent complete dataset available at the time the research was undertaken in 2016.

Data was sourced through the Department of Health & Human Services Victorian Admitted Episodes Dataset (VAED) and included a unit record and episode number, admission and discharge information, morbidity data including prefix codes and Classification of Hospital-Acquired Diagnoses (CHADx) assignment. CHADx codes were provided as part of the VAED. The Australian Commission on Safety & Quality in Health Care algorithm that mapped the classification to the ICD-10-AM 8th edition was used to calculate the incidence of individual CHADx.

2.2 Ethics Approval

Ethics approval was obtained from two separate sources, La Trobe University Science, Health and Engineering College Human Ethics Sub-Committee and Hospital X through their partnership with a larger Regional Victorian Hospital Ethics Committee. A unique identifier was used to maintain privacy and confidentiality.

3. Results

3.1 Summary of CHADx A total of 10,567 admissions were recorded at Hospital X for the five years from 1 July 2010 to 30 June 2015. Within the dataset 1,182 CHADx were recorded, 710 episodes (6.72%) documented at least one CHADx with 97.75% of all CHADx episodes occurring in multi-day stay patients. One in eight multi-day stay patients experienced at least one CHADx. The mean number of CHADx per episode for patients identified as having at least one CHADx was 1.66.


Table 1. Summary of CHADx Same Day

Admission Multi Day Admission

Total

Total Admissions 2010/11 to 2014/15 4,990 5,577 10,567 Number Admissions with CHADx 16 694 710 % Admissions with CHADx 0.32% 12.44% 6.72% Total Number Recorded CHADx 18 1,164 1,182 Mean Number CHADx per admission identified as having a CHADx 1.13 1.68 1.66

Table 2. Summary of Major CHADx (M CHADx) by Year Hospital X CHADx Description 2010/11 2011/12 2012/13 2013/14 2014/15 Total % M CHADx1 Post Procedural Complications 16 20 13 11 18 78 6.60% M CHADx2 Adverse Drug Events 6 12 7 14 9 48 4.06% M CHADx3 Accidental Injuries 20 17 17 20 11 85 7.19% M CHADx4 Specific Infections 4 9 3 5 5 26 2.20% M CHADx5 Cardiovascular Complications 32 32 23 20 31 138 11.68% M CHADx6 Respiratory Complications 15 12 10 12 5 54 4.57% M CHADx7 Gastrointestinal Complications 45 64 35 40 30 214 18.10% M CHADx8 Skin Conditions 33 40 22 9 7 111 9.39% M CHADx9 Genitourinary Complications 26 22 16 21 25 110 9.31% M CHADx10 Hospital-acquired Psychiatric States 9 8 17 9 4 47 3.98% M CHADx11 Early Pregnancy Complications 0 0 0 0 0 0 0.00% M CHADx12 Labour, Delivery & Postpartum Complications 0 0 0 0 2 2 0.17% M CHADx13 Perinatal Complications 0 0 0 0 0 0 0.00% M CHADx14 Haematological Disorders 10 7 16 7 8 48 4.06% M CHADx15 Metabolic Disorders 13 20 17 10 3 63 5.33% M CHADx16 Nervous System Complications 0 1 1 2 2 6 0.51% M CHADx17 Other Complications 31 33 40 26 22 152 12.86% 260 297 237 206 182 1182 100.00%


3.2 Summary of Major CHADx The incidence of CHADx was consistent across the five-year period with 2011/12 recording the highest number of 297 (25.12%) and 2014/15 recording the lowest number of 182 (15.40%).

Hospital X does not provide birthing services so it was expected that no CHADx were recorded in Major CHADx (M CHADx) 11 (Early pregnancy complications) and M CHADx 13 (Perinatal complications). The two cases recorded in M CHADx 12 (Labour, Delivery and Postpartum Complications) related to the same patient who presented in early labour on two separate occasions and was subsequently transferred to higher level of care.

Figure 1. Total Major CHADx Major CHADx 7 (Gastrointestinal Complications) recorded the highest number of CHADx with 214 (18.10%) of all CHADx recorded. Major CHADx 17 (Other Complications) recorded 152 (12.86%), Major CHADx 5 (Cardiovascular Complications) 138 (11.68%), followed by Major CHADx 8 (Skin Conditions) 111 (9.39%) and Major CHADx 9 (Genitourinary Complications) 110 (9.31%).

3.3 Post-Procedural Complications Seventy-eight (78) post-procedural complications were recorded, refer Major CHADx 1, equating to 6.60% of all hospital-acquired diagnoses (refer Table 2). Post-procedural complications are defined as conditions arising as a result of surgery and other invasive procedures. Across the five-year period an average of 2.47% of patients having a procedure at Hospital X suffered a post-procedural complication. This is particularly relevant given the focus of the Victorian Department of Health & Human Services during recent years on whether elective surgery can or should be undertaken safely in small rural health services (Victorian Department of Health, 2014a).

Table 3. Post-Procedural Complications Post-Procedural Complications 2010/11 2011/12 2012/13 2013/14 2014/15 Total Major CHADx1 16 20 13 11 18 78 Total No. Procedures 558 661 672 666 605 3162 % Post-Procedural Complications 2.87% 3.03% 1.93% 1.65% 2.98% 2.47%

7848

85

26

138

54

214

111 110

47

0 2 0

4863

6

152

0255075

100125150175200225

Total M CHADx 2010_11 to 2014_15


3.4 Comparison of Top Victorian CHADx and Hospital X CHADx Table 4 compares Hospital X and Victorian state-wide CHADx sub-classes, excluding maternity and perinatal complications (Jackson et al., 2009, p. 547). Hospital X experienced higher rates of nausea and vomiting, hypotension, constipation, urinary tract infection, dermatitis, rash and other skin effects. Table 5 lists the most common CHADx conditions across the five-year period.

Table 4. Top CHADx subclasses, by volume of hospital-acquired diagnoses Top CHADx subclasses, by volume of hospital-acquired diagnoses Victoria

2005-06 Hos X 2010-15

CHADx Description Victoria Total CHADx

% Hospital X Total CHADx

%

15_2 Electrolyte disorders without dehydration diagnoses 18010 4.73% 24 2.03% 5_3 Cardiac arrhythmias, conduction disturbances and

abnormal heart beat 14254 3.74% 45 3.81%

5_6 Hypotension (not drug induced) 12172 3.20% 58 4.91% 7_5 Nausea and vomiting 10234 2.69% 72 6.09% 9_2 Urinary tract infections 8467 2.22% 56 4.74% 7_4 Constipation 7232 1.90% 73 6.18% 8_3 Dermatitis, rash and other skin effects 6517 1.71% 75 6.35% 10_4 Alterations to mental state 6374 1.67% 32 2.71% 1_4 Other haemorrhage and haematoma complicating a

procedure 6332 1.66% 17 1.44%

9_4 Other complications and symptoms of the urinary system

5678 1.49% 28 2.37%

6_3 Acute lower respiratory infections (including influenza and pneumonia)

5668 1.49% 18 1.52%

All other CHADx 279895 73.50% 684 57.87% Total CHADx 380833 100.00% 1182 100.00%

Source: Jackson et al. (2009, p. 547) Box 4

Table 5. Hospital X Top CHADx by Volume

No

CHADx Description No. Episodes % Total WWHS CHADx

1 8_3 Dermatitis, rash and other skin effects 75 6.35% 2 7_4 Constipation 73 6.18% 3 7_5 Nausea and vomiting 72 6.09% 4 5_6 Hypotension 58 4.91% 5 9_2 Urinary tract infection 56 4.74% 6 3_3 All other falls 52 4.40% 7 14_2 Other hospital-acquired anaemia 47 3.98%

8 5_3 Cardiac arrhythmias, conduction disturbances & abnormal heart beat 45 3.81%

9 7_1 Gastroenteritis 34 2.88%

10 10_4 Alterations to mental state (delirium, hallucinations) 32 2.71%

Total 544 46.02%


3.5 Comparison by Volume of Major CHADx Groups Victoria and Hospital X

Jackson et al. (2009, p. 546) suggests that low volume hospitals can use major ‘roll-up’ groups for the routine monitoring of hospital-acquired conditions. Table 5 compares the percentage of major CHADx at Hospital X with percentage rates Victoria wide, excluding labour, delivery, antenatal and perinatal complications, which are not provided at Hospital X. Rates were comparable with the exception of gastrointestinal complications and skin conditions both of which were higher at Hospital X and metabolic and post-procedural complications which were lower. The increased rates of these two major CHADx groups can be mapped back specifically to the higher levels of nausea, vomiting, constipation, rash and dermatitis diagnoses as identified in Table 4.

Table 6. Major CHADx groups, by volume of hospital-acquired diagnoses Victoria 2005/06 Hospital X 2010/11-

2014/15 M CHADx

Description Number CHADx

% Number CHADx

%

5 Cardiovascular complications 38186 14.72% 138 11.68% 17 Other complications 30743 11.85% 152 12.86% 7 Gastrointestinal complications 29351 11.32% 214 18.10% 15 Metabolic complications 28899 11.14% 63 5.33% 1 Post procedural complications 26840 10.35% 78 6.60% 9 Genitourinary complications 22769 8.78% 110 9.31% 6 Respiratory complications 19885 7.67% 54 4.57% 2 Adverse drug events 12131 4.68% 48 4.06% 8 Skin conditions 12085 4.66% 111 9.39% 14 Haematological complications 11153 4.30% 48 4.06% 10 Hospital-acquired psychiatric states 10923 4.21% 47 3.98% 4 Specific infections 8701 3.35% 26 2.20% 3 Accidental injuries 5067 1.95% 85 7.19% 16 Nervous system complications 2615 1.01% 6 0.51% All CHADx classes 380833 100.00% 1182 100.00% Maternity and Neonatal CHADx 121485 31.90% 2 0.17% Total Excluding Maternity & Neonatal

CHADx 259348 68.10% 1180 99.83%

Source: Jackson et al. (2009, p. 546) Box 3

3.6 Patient Safety CHADx

Five episodes across the five-year timeframe were recorded in a special group of CHADx defined as being of “particular relevance for efforts to improve patient safety” (Jackson et al., 2009, p. 546).

Table 7. Low Volume CHADx CHADx Description WWHS

Total 1_2 Gas Embolism 0 3_4 Injury due to Assault 0 1_6 Foreign body or substance left following procedure 0 3_2 Falls with Intracranial Injury 4 2_17 Anaphylactic shock due to correct drug properly administered 1

Source: Jackson et al. (2009, p. 546)


4. Discussion

Until now using administrative data to identify and monitor adverse events occurring during periods of hospitalisation has had limited application, with research centred around large data sets (Jackson, Fong, et al., 2011; Jackson et al., 2009; Trentino et al., 2013), or targeted studies looking at patients admitted with specific diagnoses (Cromarty et al., 2014; Kable et al., 2009). No research has been found testing if administrative data is successful at identifying a broad cross-section of adverse events at a local level.

Frequently occurring CHADx with a higher rate than corresponding state-wide levels were able to be identified very readily through analysis (refer Table 4 & 5). Seventy-five episodes of dermatitis and rash were documented during the period, seventy-three cases of constipation and seventy-two episodes of nausea and vomiting. These issues were then forensically analysed at an individual episode level to ascertain underlying causes and restorative actions implemented.

Administration of codeine-based pain relief for post-operative patients was reviewed and changes implemented leading to a substantial reduction in constipation and nausea. Similarly, the use of plastic mattress protectors was reviewed leading to a decrease in the incidence of dermatitis and rash, a particular problem for longer staying patients. None of these issues had previously been identified as a problem until the publication of this study and had been treated in isolation with no investigation as to whether such cases were part of a broader problem.

Jackson, Ngheim, et al. (2011, pp. 144-145) suggest that mundane complications of care are often overlooked when setting patient safety priorities, in spite of the substantial health care costs associated with such conditions, extended lengths of stay and added patient pain and discomfort. Table 5 identifies that at least eight of the ten most common hospital-acquired conditions at Hospital X would fall into the category of mundane complications. Using the CHADx tool provided the organisation with the capacity to identify the prevalence and frequency of such conditions, investigate the causes, and build processes into the clinical governance framework to reduce prevalence in the future.

Jackson et al. (2009, p. 546) highlighted five CHADx considered to have particular relevance in regard to patient safety and which despite their expected low numbers were maintained as separate sub-classes, less than 100 cases in the sample of more than 2 million episodes. Table 6 reveals that Hospital X had five episodes within the special CHADx group during the study period being considered, which is relatively high when compared to the 2005-06 Victorian State-wide data.

Analysis showed that not all of the five cases at Hospital X had been followed up with a full root cause analysis, illustrating the issues inherent in relying on incident reports to capture such events. Due to the infrequency of such events clinical governance frameworks could be adjusted to monitor these special CHADx monthly to ensure such episodes had been identified and investigated in accordance with incident reporting protocol relating to serious adverse events. Other very high risk CHADx could be added to develop a suite of indicators collected and reported regularly to Clinical Governance Committees.


5. Conclusion

Using the CHADx tool as a means of identifying hospital acquired conditions and adverse events through the routinely collected Victorian Admitted Episodes Dataset has application in a small health care setting. It is only by using the CHADx methodology that Hospital X identified a number of conditions which are occurring regularly and while not life threatening is adding to health care costs and creating unnecessary patient discomfort and pain.

The true value of CHADx for individual health care providers may be the timely and cost-effective identification of specific serious complications, targeted so they are relevant and appropriate to the size and casemix of the organisation and benchmarked with similar facilities, and mundane and common complications that are not presently being documented through other clinical governance monitoring processes.

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Health Information Management in Australia: findings from the 2018 Australian HIW Census Kerryn Butler-Henderson1, Kathleen Gray2, Ann Ritchie3, Angela Ryan4, Julie Brophy5, Christopher Pearce6, Louise K Schaper7, Vicki Bennett8

1University of Tasmania, Launceston, Australia, 2University of Melbourne, Parkville, Australia, 3Health Libraries Australia, Melbourne, Australia, 4Australian Digital Health Agency, Sydney, Australia, 5Department of Health & Human Services, Melbourne, Australia, 6Australasian College of Health Informatics, Melbourne, Australia, 7Health Informatics Society of Australia, South Melbourne, Australia, 8Health Information Management Association of Australia, North Ryde, Australia

1. Introduction

The Health Information Management profession has a long history in Australia, celebrating in 2019 the 70th anniversary since the establishment of the professional body (New South Wales Association of Medical Records Librarians and the Victorian Association of Medical Librarians) [1]. Over time the profession has transitioned from being termed Medical Records Librarians to Medical Record Administrators, and then Health Information Managers (HIMs). Several health reforms have required the profession to transform to continue to meet the needs of industry. Early hospital-based training programs were replaced with Diploma level tertiary education training, with bachelor’s degrees introduced in the 1980s. The 1997 Australian Standard Classification of Occupations (ASCO), Second Edition included HIM as a profession. The ASCO later became the Australian and New Zealand Standard Classification of Occupations (ANZSCO), with HIM listed at the unit group level (lowest classification level), 2242 Archivists, Curators and Records Managers (HIM - 224213) [2]. This classification enabled the collection of data about the number of people working in the profession who identify as a HIM. In 1992 the Health Information Management Association of Australia (HIMAA) developed a set of competencies that reflect the skills and knowledge required for a graduate, which was updated over the years, most recently in 2017 [3].

Whilst the Australian Bureau of Statistics (ABS) captures information about the workforce through its Census of Population and Housing, conducted every five years, the ABS does not report occupation data at the unit group level, and only captures demographic data, such as age, gender and location. As such, there has been no empirical evidence produced about the Health Information Management workforce. Graduate data is not publicly released, and many alumni are lost to follow-up, so we are unable to measure the graduate pathway through the profession. The above-mentioned HIMAA competencies are updated by a HIMAA committee of HIM educators, with open public comment, instead of being based on real world evidence. Consequently, there is no systematic monitoring of the profession to enable workforce planning.

This need for open data collection and reporting of the profession has been reported by Health Workforce Australia [4], a 2015 focus group [5], and two workforce summits [6-7]. The first agreed actions from the 2016 Health Information Workforce (HIW) Summit was “We need a census of the health information workforce and regular collection of data” [7]. The Australian HIW Census tool was developed between 2016-2018 [8], with the first Australian census held in May 2018. The


census summary report was published in November 2018 [9]. Whilst that report includes the analysis of the broader workforce, this paper presents the results from an analysis of the occupation Health Information Management.

2. Methods

Anyone who self-identified as part of the health information workforce, including those who “work (including volunteer or actively seeking) in a role where the primary function is related to developing, maintaining, or governing the systems for the management of health data, health information, or health knowledge… for/with an organisation that operates in Australia, your role relates to the Australian operations, and relates to the health sector” [10] could complete the census. The census project obtained ethical approval from the University of Tasmania Social Science Human Research Ethics Committee (#H0017281), with consent captured at the start of the census and a privacy statement and Data Management and Access Policy are available on the census website [10].

To identify respondents who would be classified within the occupation Health Information Management, specific criteria with regards to their qualification or job title were developed. Specifically, a respondent was included in this analysis if they met one of the following criteria:

Identified their occupation group as Health Information Manager from the list of occupations provided, OR

Included one of the following terms in their self-identified occupation group or job title: [health information OR HIM OR medical record OR health record], OR

Included one of the following terms in their formal education qualification: [health information OR HIM OR medical record OR MRA], OR

Identified they are a Certified Health Information Manager, which requires a formal qualification in HIM.

As this analysis was only examining HIMs, the above criteria did not include clinical coders, unless they met the above criteria. Eligible responses were extracted from the census database into an MS Excel spreadsheet and imported into IBM® SPSS v25.0 for analysis (descriptive statistics and Pearson’s Chi Square Test of Independence).

3. Results

A total of 710 respondents to the 2018 HIW Census met the eligibility criteria and are included in these results. The occupational and employment characteristics of respondents are summarised in Table 1. Whilst the eligibility criteria defined the occupation group Health Information Management (education and/or job title in HIM), only 69.5% (430/619 who were able to categorise their occupation) of respondents self-identified their occupational group as HIM. There was a significant association between identifying occupation as HIM and identifying the major occupational group as Manager (Χ2(2) = 125.5, p < .001).


The average age of respondents is 44.0 years (range 21-78 years), with 50.1% (356) aged 45 years or older. 87.7% (623) of respondents identified their gender as female. The majority (96.1%, 682) of respondents are Australian citizens, with 2.7% (19) as permanent residents and theremaining 1.2% (9) respondents on a temporary visa. A small (3.2%, 23) proportion of respondentsare registered health practitioners, including dental, medical, nursing, and allied healthpractitioners. The majority (57.6%, 409) of respondents are a member of a professional associationor society, including the Health Information Management Association of Australia (HIMAA) (49.4%,351), Health Informatics Society of Australia (HISA) (9.7%, 69) and the Clinical Coders Society ofAustralia (CCSA) (3.4%, 24).

Over half of respondents (52.1%, 370) reported their highest health information qualification was a bachelor’s degree. Nearly a quarter (24.1%, 171) of respondents reported their highest qualification is a master’s degree, and ten (1.4%) responded they have a doctoral qualification. Nearly a tenth (7.2%, 51) of respondents do not possess a health information qualification. The highest percentage of tertiary qualifications were gained at the universities that have previously offered or currently offer a qualification in health information management: La Trobe University (30.0%, 213), Curtin University (11.1%, 79), University of Sydney (10.7%, 76), Queensland University of Technology (QUT) (9.0%, 64), Lincoln Institute of Health Sciences (4.5%, 32), and Cumberland College of Health Sciences (4.1%, 29). The average time since completing the highest qualification was 15.5 years (range 0-55 years). There was a significant association between the highest qualification and length of time since completing the highest qualification (Χ2(6) = 124.6, p < .001), with master’s qualifications being completed more recently. Furthermore, there was a significant association between the highest qualification and aged 45 years or older (Χ2(1) = 20.4, p < .001), with people under 45 years more likely to possess a master’s qualification.

With regards to professional development (PD), 79.9% (567) of respondents indicated they have undertaken some form of PD in the last 12 months. Work-based learning was the most popular form of PD (73.2%, 415), with 39.0% (221) of respondents undertaking professional activities, 37.6% (213) engaging in self-directed learning, and 15.0% (85) completing formal education. However, whilst the majority of the HIM workforce maintain their professional development, only a quarter (23.7%, 168) of respondents currently maintain some form of certification, including Certified Health Information Manager (13.8%, 98), Certified Health Informatician Australasia (3.7%, 26), and Certified Health Information Practitioner (0.7%, 5).

The census captures the data from those currently working in health and those working elsewhere or not currently in paid employment. 92.1% (654/688) of respondents are currently working in a health information role, with 10.7% (76) currently working in both a health information and another non-health information role. Furthermore, 5.5% (39) of respondents stated they are currently working in at least two health information roles. 15.6% (111/688) of respondents are currently actively looking for work. Of those in paid employment in health information, 65.1% (426/654) are in full-time employment (>35 hours per week paid employment for a specific role) [11], with 78.6% (558/654) reporting they are working in a permanent position. 61.0% (399/654) reported working unpaid overtime in the last week.

Whilst the majority of respondents identified they work in Victoria (44.6%, 290/650), this may be due to promotion of the census to the workforce in Victoria compared to elsewhere in Australia. However, there is again a pattern of higher numbers in states where a HIM course is or has been


offered compared to other states. A small number (<5) reported they work in a role which requires them to travel across at least two states. Nearly two-thirds (63.4%, 450/654) of respondents are working in the public sector and working in a hospital (67.8%, 442/654). More than half (52.6%, 299/568) of respondents reported weekly remuneration before tax as between $1,000-$1999. No association was found between education level and remuneration (Χ2(7) = 10.9, p = 0.140).

Respondents were also asked to identify which domains of competencies they require to perform their health information role, using the five domains from the CHIA competencies [12]. The responses are summarised in Table 2.

Lastly, the intentions of those still working in the HIW were examined. Only 9.0% (48/533) of respondents indicated they plan to leave the workforce in less than 5 years. However, 25.98% (138) indicated they plan to leave in the next 5-10 years, resulting in a loss of more than a third (34.9%, 186) of the workforce in the next 10 years. Another third (33.6%, 176) indicated they plan to leave in the next 11-20 years and the last third (31.5%, 168) indicated they plan to remain in the workforce for more than 20 years. There was a significant association between being aged 45 years or older and intention to leave the workforce in less than 10 years (Χ2(2) = 203.1, p < .001). Given the association between age and qualification level, it is postulated more of the workforce will possess a masters level qualification in the next ten years.

Table 1. Occupational and employment characteristics Employment Characteristic

Responses Number (n) Percentage

Occupational category (n=587)

Health information manager 430 69.5% Clinical coder 81 13.1% Health informatician or technology specialist 64 10.4% Data analyst 39 6.3% Costing analyst 5 0.8%

Experience in HIM in Australia (n=688)

Average years 17.0 years Range years 1-45 years

Major employment group (n=688)

Manager 320 45.1% Professional 282 39.7% Clerical or Administrative Worker 65 9.2% Technician, Community or Sales Worker <10 -

Current in paid employment (n=688)

Health informatics role 578 81.4% Health informatics role & another health role 76 10.7% Another health role 22 3.1% Non-health role 7 1.0% Not currently in paid employment 5 0.7%

Full-time employment (n=654)

Yes 426 65.1% No 228 34.9%

Employment Status (n=654)

Permanent 558 78.6% Contract 79 11.1% Casual 11 1.5% Self-employed 6 0.8%

Unpaid overtime in last week (n=654)

Yes 399 61.0% No 255 39.0%

State/Territory (n=650)

Victoria 290 44.6% New South Wales 146 22.5% Queensland 92 14.2% Western Australia 41 6.3% South Australia 28 4.3% Australian Capital Territory 25 3.8% Tasmania 21 3.2% Northern Territory 7 1.1%


Employment Characteristic

Responses Number (n) Percentage

Organisation Status (n=654)

Public 450 63.4% Private 131 18.5% Public/Private partnership 18 2.5% Not for Profit 55 55%

Organisation Type (n=654)

Hospital 442 67.8% State health department 44 6.7% Local health service 27 4.1% Federal health organization 16 2.5% Community health care service 15 2.3% Primary care or primary health network 14 2.1% Educational facility 13 2.0% Health technology organisation 12 1.8% Insurance organisation 11 1.7% Other public/government organisation 28 4.3% Other private organisation 18 2.8% Other not for profit organisation 14 2.1%

Weekly wage before tax (n=568)

<$1000 62 10.9% $1000-$1449 156 27.5% $1500-$1999 143 25.2% $2000-$2499 105 18.5% $2500-$2999 43 7.6% $3000-$3499 24 4.2% >$3500 35 6.2%

Table 2. Domains of competencies required to perform role (n=710)

Domain Description Number Percentage

Health and Biomedical Science

Healthcare systems and practice and basic biomedical science concepts

297 41.8%

Human and Social Science

Human and social context related to healthcare and the systems of healthcare including issues of clinical practice, consumers and legal requirements

320 45.1%

Information and Communication Technology

Information technology in general, not limited to healthcare, though the principles certainly apply to healthcare

452 63.7%

Data and Information Science

Data and information systems and services, not limited to healthcare, though the principles certainly apply to healthcare

492 69.3%

Management Science Governance and management of services and systems development, change management, knowledge management, business practices and organisational strategy at all levels

422 59.4%

4. Discussion

The criteria for this analysis were to identify respondents to the 2018 Australian HIW Census belonging to the occupation Health Information Management. Of the 710 eligible respondents, only 69.5% of them explicitly self-identified as part of this occupation, showing the career outcomes are diverse. The occupation group is a predominantly female, older workforce consisting of largely Australian citizens. Whilst the occupation is listed on both the Training Visa Occupations List and


the Visa Occupations List, the findings suggest there are very few people working in the Australian workforce who have entered Australia under either visa category. This presents an opportunity for the workforce to address reported shortages [4]. Similarly, only a small proportion of respondents (3.2%) are registered health practitioners, and very few practitioners work more than one health information role (5.5%) or in both a HIM role and other health role (10.7%). Health practitioners seeking to retrain but remain in the health industry are another source of candidates, and in the future as digital health training increases in health degrees, it will create opportunities for conjoint appointments. Workforce capacity is an immediate concern, with a quarter of the workforce reporting they intend to leave in the next ten years.

Retraining has resulted in a greater number of younger respondents undertaking further formal education, such as a master’s qualification. Furthermore, there is a high level of ongoing annual professional development in this occupation, with nearly 80% of respondents undertaking some form of PD in the last 12 months. This is important to maintain contemporary skills and knowledge. Yet only a quarter of respondents seek formal recognition of this currency through credentialing. Peak bodies need to explore why practitioners are not seeking a credential, modify existing programs to meet stakeholder needs, and promote the benefits of credentialing to practitioners and employers.

The majority of this workforce reported working full-time hours (65.1%). Unpaid overtime is a major issue in this workforce, with 61.0% of respondents reporting they have worked unpaid hours of overtime in the last week, compared to the Australian average of 36% of the workforce [13]. Further research is required to understand why there is a disproportionately high percentage of the workforce performing unpaid overtime.

Further research is also required to examine the changing nature of the HIM role. The findings of the domains of competencies show a higher proportion of respondents undertaking functions related to “Data and Information Science” and “Information and Communication Technology” instead of the traditional “Management Science”, which has differentiated HIM from other disciplines in the health information workforce. This is further evidence of the diversity now seen in this occupation.

5. Conclusion

The Health Information Management occupation has a long history supporting Australian healthcare delivery through information management over more than 70 years. During this time, the profession has transformed to meet health reform needs, taking up diverse responsibilities in the health system. However, the findings presented in this paper shows this is an older workforce, which will have a significant attrition rate if strategies are not developed and implemented today to meet the needs of the future. In short, this occupation needs to harness its strength in diversity if it is to evolve and play a key role in the future.


References

Health Information Management Association of Australia, HIMAA History, 2016, http://www.himaa2.org.au/index.php?q=node/1895.

Australian Bureau of Statistics (ABS), 1220.0 - ANZSCO - Australian and New Zealand Standard Classification of Occupations, First Edition, Revision 1, 2009, http://www.abs.gov.au/ausstats/[email protected]/Previousproducts/1220.0Search0First%20Edition,%20Revision%201.

HIMAA, Health information manager competency standards, 2017, himaa2.org.au/sites/default/files/HIMAA_HIM_Competency_Standards_Version_3_FNL_June2017.pdf.

Health Workforce Australia, Health information workforce report, 2013, http://industry.gov.au/Office-of-the-Chief-Economist/SkilledOccupationList/Documents/2015Submissions/HIMAA-Attachment-4.pdf.

K. Butler-Henderson, R. Lawrance, S. Low, N. Donnolley, & J. Lee, Researching the healthinformation workforce, Health Information Management – Interchange, vol. 6 (2) (2016), pp. 32-35.

HIMAA, Health information workforce summit report 2015, 2016, http://www.himaa2.org.au/index.php?q=node/2898.

HIMAA, Health information workforce summit 2016 summary and outline of agreed actions, 2016, http://himaa2.org.au/index.php?q=node/3199.

K. Butler-Henderson, K. Gray, D. Greenfield, S. Low, C. Gilbert, A. Ritchie, M. Trujillo, V. Bennett,J. Brophy, & L.K. Schaper, The development of a national census of the health informationworkforce: expert panel recommendations, Studies in Health Technology and Informatics, 239(2017), 8-13.

K. Butler-Henderson & K. Gray, Australia’s health information workforce: census summary report2018, University of Tasmania, 2018.

K. Butler-Henderson & K. Gray, Health information workforce census website, 2018,http://www.utas.edu.au/business-and-economics/hiwcensus.

ABS, 6102.0.55.001 - Labour Statistics: Concepts, Sources and Methods, 2018, https://www.abs.gov.au/ausstats/[email protected]/Lookup/by%20Subject/6102.0.55.001~Feb%202018~Main%20Features~Employment~4

Certified Health Informatician Australasia, Health informatics competencies framework, CHIA, 2013.

M Wooden, The changing nature of work and worker wellbeing, Australian Institute of Health & Welfare, 2017, https://www.aihw.gov.au/getmedia/ac1e8df0-4f19-4c59-9df8-3211c395bd3f/aihw-australias-welfare-2017-chapter4-1.pdf.aspx.


Introducing statistical sampling and quantitative analysis to the audit of clinical coding Paraic Bergin1, Carolyn Madigan1, Riette Joubert1

1Hamad Medical Corporation, Doha, Qatar

Abstract

Introduction: Hamad Medical Corporation (HMC) is the largest healthcare provider in Qatar and has over 320,000 patient discharges each year. As part of the preparations for the introduction of a Social Health Insurance Scheme (SHIS) and a move to Activity Based Funding (ABF), a corporation wide audit of clinical coding accuracy and compliance with mandatory Patient Minimum Data Set (Patient MDS) requirements was carried out.

Method: The audit tested a sample of 1,829 discharges selected at random from an un-stratified population of 171,827 cases. The sample was calculated at a 99% confidence level with a margin of error of 3%.

Results: The overall accuracy of clinical coding was 66%, i.e. coding errors were detected in 623 of the 1,829 encounters audited. A total of 1,301 coding errors were detected in these 623 encounters audited, giving an average of 2.088 coding errors per encounter with errors. The rate of incorrect AR-DRG assignment was 7.5% including a 1% rate of invalid admissions. 28% of the sample had major coding errors and these accounted for 71% of all errors found. 12% of the sample had incorrect principal diagnosis and 44% of all major errors were found in this group. Of the eight Patient MDS items tested, significant non- compliance was detected in three.

Discussion: This is the first audit of clinical coding accuracy and Patient MDS compliance in HMC to use statistical methods for sample selection and results analysis. It is also the first comprehensive audit across the corporation. Accordingly, there are no comparative data available. However, the results indicate clearly areas that need improvement and provide a benchmark against which future audits can be measured.

Conclusion: The study introduced statistical sampling and auditing of clinical coding to a major healthcare provider. The results show clearly the areas where improvement is most needed to support the preparations for the introduction of insurance and/or Activity Based Funding.

1. Introduction

Hamad Medical Corporation (HMC) in Qatar serves a population of over 2.7 million people through a countrywide network of 16 general and specialist hospitals and several satellite facilities. It employs over 28,000 staff and operates the national ambulance and air ambulance service as well as the national blood bank. Government owned and operated, HMC is both


the premier provider and provider of last resort. It is the largest healthcare organisation in the country and provides 100% of all trauma and emergency services, tertiary level services and over 80% of all other hospital based care (HMC 2017 Annual Report). Admitted patient activity is coded in ICD-10-AM 7th Edition and HMC discharges more than 320,000 patients annually. Approximately 180,000 patients are admitted for same-day medical or surgical treatment and over 140,000 are admitted as inpatients. Auditing of clinical coding was done on an ad-hoc basis. There were no systems or formal processes in place and 5% of the caseload was assumed to be an appropriate sample size. In 2014, HMC commenced implementation of a new Clinical Information System, including moving from paper based to electronic medical records and this process completed in 2016. Health Information Management and clinical coding functions were operated and managed within each facility until 2018, when a process of consolidating into 2 units began. This process of amalgamation is continuing throughout 2019. Healthcare in Qatar is heavily subsidised, with 98% of the total cost of HMC services being funded by the government. In 2013, the first phase of a National Health Insurance Scheme (NHIS) was introduced with the aim of reducing government investment and increasing cost to employers and the private sector. The NHIS operated until December 2015 when it was suspended, to be restructured. The new scheme, the Social Health Insurance Scheme (SHIS) has been delayed and no date has yet been set for insurance to resume. (Qatar National Health Strategy 2018-2022). In 2018, the HMC Commercial Management department, as part of the preparations for the SHIS and Activity Based Funding (ABF), designed and carried out a statistically based audit of clinical coding accuracy and Patient Minimum Data Set (Patient MDS) compliance across the entire corporation, the first such audit in the history of HMC.

2. Aims/Objectives

The principal purpose of the audit was to measure the quality of clinical coding and levels of compliance with key MDS items that could impact claims in the proposed Social Health Insurance Scheme (SHIS) and/or organization budgets in an Activity Based Funding (ABF) environment. The secondary aim was to measure clinical coder accuracy and productivity.

3. Method

The audit was covered in this study was comprised of two parts: 1. Audit of clinical coding accuracy for discharges in the period, excluding same-day dialysis

cases;2. Audit of compliance with Patient Minimum Data Set requirements.

The audit was planned in November and December 2017 and the period selected for the main audit (of clinical coding accuracy and MDS compliance) was the first nine months of 2017, i.e. 1 January to 30 September 2017. Further additions were made to the audit period and populations for some of the Patient MDS tests, but these did not affect the main audit. The department recruited a team of five experienced clinical coders and coding auditors (HIM degree holders and/or HIMAA certified and with more than five years coding experience) from within the corporation and trained them in statistically based audit sampling in the final quarter of 2017. The principal population for the main audit (Population A) was the 171,827 discharges from all HMC facilities between 1 January and 30 September 2017. A sample size of 1,829 cases was calculated based on a confidence level of 99% and a confidence interval of +/- 3%, i.e. there


would be a 1% or lesser chance that the population would differ from the sample results by more than 3%. The sample was chosen at random from the un-stratified population and tested for correlation using the Pearson Product-Moment Correlation Coefficient. This measures the degree to which two items are correlated. Correlation may be positive (+ value) or negative (- value). A correlation coefficient of zero means the items are not correlated. A positive correlation indicates that as values increase in one data item, there is a corresponding increase in the second item being measured. A negative correlation indicates that as one item increases, the other value decreases. Correlation may also be strong (Value >0.5 or <-0.5) or weak (Value <0.5, >-0.5). Perfect positive correlation is equal to 1. This means that for every increase of ‘n’ in the value of ‘x’, there is a corresponding increase of ‘n’ in the value of ‘y’. The Pearson Product-Moment Correlation Coefficients for the sample vs. Population A were:

(a) Analysed by AR-DRG: +0.985

(b) Analysed by hospital: + 0.999


(c) Analysed by clinical coder: + 0.967

(d) Analysed by clinical coder qualification: +0.999

In the items tested above, very strong positive correlation is found. The weakest correlation found was in the comparison of population by coder to sample by coder (+0.967) and this showed that the volume of the sample represented by each coder was equal to 96.7% of the volume of the population represented by that coder, i.e. if a coder has coded 10% of the population being sampled, we can generally expect that coder’s cases will represent 9.67% of the sample.

The clinical coding audits were conducted using the “blind” method, i.e. the auditor coded each encounter tested without sight of the original coding. Each encounter audited was further reviewed by another coding auditor. Discrepancies/ disagreements not resolved between the auditor and reviewer were adjudicated by the Senior HIM Advisor.


The detailed results of the cases where coding errors were detected were shared with the coding unit for each facility and responses invited. The audit team met with the coding unit managers to review and discuss each case where errors were found. Following this review, the audit results were finalised.

Audit results were compiled and analysed under several categories. The principal analysis tool used was the Statistical Process Control Chart (Shewart, 1931). For each analysis, the population or sample mean, standard error and upper control limits were calculated. Results were plotted against the sample mean and the upper control limit (equal to the mean plus 3 standard deviations from the mean). The lower control limit was not considered, as in all cases this was a negative value and the minimum value for any item in these tests was zero.

Variations from the mean arising between zero and the upper control limit are regarded as common cause variations while those above the upper control limit are regarded as special cause variations. Common cause variation is generally reduced by addressing the causes of variation common to the items, i.e. by improving the process. Special cause variation is generally reduced by identifying the root cause and addressing or eliminating it. A process in which all items measured fall between the upper and lower control limits (or in these cases, between the upper control limit and zero) are regarded as being in statistical process control, i.e. there are no outlier variances and further improvement is achieved by improving theprocess being measured. These analyses provided evidence of processes operating withinstatistical control as well as identifying special cause variations for further investigation.

The categories examined were a) each of Population A, the audit sample, encounters with errors and encounters with

incorrect AR-DRG assignment analysed by AR-DRG; and

b) each of the audit sample, encounters with errors, average number of errors per encounterin the sample, and encounters with incorrect AR-DRG assignment analysed by clinicalcoder.

4. ResultsThe overall level of coding accuracy found was 66%. A total of 1,301 coding errors were found in 623 encounters in the sample of 1,829 cases audited while 1,206 encounters (66% of the sample) had no coding error. A further 110 cases had only minor errors. However, 513 discharges audited (28% of the sample) had major errors and 930 major errors were found in these cases (71% of all errors). 12% of encounters tested had incorrect principal diagnosis and accounted for 44% of all major errors.

A summary and analysis of the clinical coding errors found are in Table 1 and Table 2 below:


Table 1. Summary of encounters by main coding error type

Table 2. Analysis of coding errors found

There were 138 cases of incorrect AR-DRG assignment, including 19 cases that did not meet the criteria for admission. Tables 3 – 6 show each of Population A, the audit sample, encounters with errors and encounters with incorrect AR-DRG assignment analysed by AR-DRG while Table 7 summarises outlier AR-DRG’s across the 4 groups. The population outliers and sample outliers are consistent with the correlation coefficients measured.


Table 3. Analysis of AR-DRGs in Population A

Table 4. Analysis of AR-DRGs in Audit Sample


Table 5. Analysis of Encounters with Coding Errors by AR-DRG

Table 6. Analysis of AR-DRG changes and Invalid Admissions


Table 7. Summary of outlier AR-DRGs found

Results were also analysed by coder and by coder grade and qualifications. Tables 8 – 11 show each of the audit sample, encounters with errors, average number of errors per encounter in the sample, and encounters with incorrect AR-DRG assignment analysed by clinical coder while Table 12 summarises results by coder grade and qualifications.

Table 8. Analysis of audit sample by coder


Table 9. Analysis of average number of Encounters with errors by coder

Table 10. Analysis of average number of Coding Errors per encounter by coder


Table 11. Analysis of average AR-DRG changes per encounter by coder

Table 12. Coder grade and qualifications.


Table 13. Patient MDS test results

5. DiscussionThis is the first comprehensive audit of clinical coding accuracy and Patient MDS compliance across the corporation. It is also the first audit to use statistical methods for sample selection and results analysis. Accordingly, there are no comparative data available.

A recent study of the use of Asia Pacific Network APN ICD-10 simplified version in Cambodia reported clinical coding accuracy levels of between 66.67% and 93.41% in a sample of 1,038 cases coded by teams from 10 hospitals (Paoin, Yeunyongsuwan, Yokobori, Endo and Kim, 2018). ICD-10-AM is significantly more complex than APN ICD-10 and against this the accuracy level of 66% in Qatar appears reasonable.

A 2018 study of 1,900 discharges audited in the Republic of Ireland, which also uses ICD-10-AM, reported 14% of cases tested had incorrect Principal Diagnosis while 7% had incorrect Principal Procedure (HSE Healthcare Pricing Office, 2018). This is comparable to the results found in HMC, notwithstanding the Irish audit samples were chosen using different methodology, i.e. not statistically based.

Major coding errors were found in 28% of the cases audited and 44% of all coding errors were found in the 12% of cases with incorrect principal diagnosis. The remaining 16% of encounters with major errors accounted for 27% of all coding errors. In an insurance scenario, these errors are more likely to be detected on submission, potentially leading to high claims rejection rates. In HMC, this level equates to approximately 64,000 encounters with major errors a year. The average cost per chart coded in HMC is c. QAR 140 (AUD$ 60). Assuming a cost of correction of QAR 700 (AUD$ 300) even a relatively low detection and rejection rate could have a significant financial impact.

The audit found 138 cases with incorrect AR-DRG assignment (7.5% of cases audited). Of these nineteen were “invalid” admissions that did not meet the criteria for admission, representing 1.04% of the sample. If this error rate holds true for the population, HMC invalid admissions should be expected to be c. 2,400 per annum. The remaining 119 cases were valid admissions where the incorrect AR-DRG assignment was due to clinical coding error. On


an annual basis, this should be expected to be in the region of 15,000 incorrect AR- DRG assignments.

A 2004-05 study of 752 inpatient discharges from a specialised surgical unit in a major teaching hospital in Melbourne, Australia reported a DRG change rate of 15.7% (Cheng, Gilchrist, Robinson and Paul, 2009) while a 2009 study of 2,486 cases in the Eastern Health metropolitan public health service in Melbourne, Australia reported 440 DRG changes (17.7%) (Uzkuraitis, Hastings and Torney, 2010). The Irish study reported 256 DRG changes in a sample of 1,900 cases audited, a rate of 13.5% (HSE Healthcare Pricing Office, 2018).

While the HMC DRG change rate of 7.5% compares favourably against these, neither of the Australian samples was randomly selected from an un-stratified population and so are not directly comparable with the HMC results. The Irish samples were selected on the basis of 100 discharges from each of the 19 hospitals surveyed, with a 70/30 split between inpatient and day-case discharges, and the results are likewise not directly comparable with HMC.

In the analysis of the errors by coder grade and qualifications, there are significant differences between the results of the different grades and qualification classes. Senior HIM/HIMAA qualified clinical coders perform better than all other groups, accounting for 41% of the encounters tested, 37% of encounters with errors, 35% of DRG changes and 34% of coding errors. HIMAA qualified coders (all grades) represent 53% of coders and 51% of the encounters audited. They account for 46% of the encounters with errors, 45% of DRG changes and 41% of coding errors. The poorest performing group are legacy coders without formal qualifications engaged before the adoption of ICD-10-AM. While this group represent 5% of coders and 5% of the cases audited, they account for 7% of encounters with errors, 9% of DRG changes and 7% of coding errors. The average error rates (rounded to whole numbers) for Senior HIM/HIMAA qualified coders per 1,000 charts coded are 308 encounters with errors, 63 AR-DRG changes and 589 coding errors. The comparable rates for coders without qualifications are 480 encounters with errors), 136 DRG changes and 996 coding errors. As the DRG change rate for coders without qualifications is 2.16 times that of Senior HIM/HIMAA qualified coders, there may be improvements possible through allocating less complex cases to coders with less qualifications and experience.

The items with the lowest compliance with Patient MDS requirements are care certification for patients with length of stay above certain limits and certification of necessity for ICU care. On examining these issues, it was found that both involve a higher requirement than normal for statistical discharge and readmission of the patient. Although the patient’s medical record is now electronic, this process is difficult and time consuming for clinicians and nursing staff. As highlighted in Guo, Chen and Mehta (2017), “this increased ‘click burden’ is a source of frustration for physicians.” This was also identified as a factor in cases where the length of stay exceeding the mandatory maximum duration of a single episode of care. The other item with relatively low compliance was admission type. Here it was found that there is a lack of clarity on the definition of elective admissions. The Patient MDS defines “elective” as admitted from a waiting list while HMC staff tend to admitted non-emergency cases as “elective.”

This audit was statistically based and the results may be extrapolated at a 99% confidence level within a margin of error of 3%. By contrast, the audit approach within the coding units was not statistically based and the results could not be extrapolated to the population with confidence. Furthermore, the audit sample derived here was 1.06% of the population being tested, compared to the units’ sample size of 5%. Adoption of statistically based sampling and quantitative analysis in the audit of clinical coding in the coding units would deliver reliable


results that could be extrapolated to the population being tested and would reduce the audit workload by almost 80%.

6. ConclusionThe study introduced statistical sampling and auditing of clinical coding to a major healthcare provider in a comprehensive audit of both clinical accuracy and compliance with mandatory Patient MDS requirements. The study produced valuable data that can be extrapolated with confidence to lead improvement efforts for the future. The results show clearly the areas where improvement may be focused to support the introduction of insurance and/or Activity Based Funding.

The study did not systemically examine the underlying records and data except for the purposes of carrying out the audit. Further study may be warranted to examine the completeness and accuracy of the underlying medical records and diagnostic reports as well as their suitability for clinical coding, and in particular to measure the impact of the introduction of an electronic health record (EHR).

The application of statistical sampling to routine audit of clinical coding will reduce the workload by up to 80% while providing a significantly higher degree of confidence in the results. Applying quantitative analysis to audit results will assist in future efforts to accurately identify and address root causes of errors in clinical coding. As the AR-DRG is a direct output of clinical coding, improvements in coding accuracy could be reasonably expected to result in more accurate AR-DRG assignment. Furthermore, variations in accuracy rates between groups of coders with differing levels of qualifications suggest improvements may be achieved by rebalancing workloads to allocate more complex cases to HIM/HIMAA qualified coders.

7. ReferencesCheng, P., Gilchrist, A., Robinson, K. M., & Paul, L. (2009). The risk and consequences of clinical miscoding due to inadequate medical documentation: a case study of the impact on health services funding. Health Information Management Journal, 38(1), 35–46

Guo, U., Chen, L. and Mehta, P.H., 2017. Electronic health record innovations: Helping physicians – One less click at a time. Health Information Management Journal, Vol. 46(3) 140-144

HSE Healthcare Pricing Office (2018) HIPE Chart based audits in 2018, Health Service Executive Healthcare Pricing Office, No. 83, 5

Hamad Medical Corporation (2018) 2017 Annual Report. HMC.

Qatar Ministry of Public Health, 2018. National Health Strategy 2018 – 2022, MOPH.

Paoin, W., Yeunyongsuwan, M., Yokobori, Y., Endo, H. and Kim, S., 2018. Development of the ICD-10 simplified version and field test. Health Information Management Journal, Vol. 47(2) 77-84

Shewart, W.A., 1931. Economic Control of Quality of Manufactured Product. Martino Fine Books, 2015 edition, paperback http://www.pqmonline.com/assets/files/lib/books/shewhart1.pdf

Uzkuraitis, C., Hastings, K. and Torney, B., 2010. Casemix funding optimisation: working together to make the most of every episode. Health Information Management Journal, Vol. 39(3) 47-49


Distributed Knowledge-based Computer-Assisted Clinical Coding System Rajvir Kaur1, Jeewani Anupama Ginige1 1Western Sydney University, Sydney, Australia

Abstract.

Free-text clinical narratives recorded during clinical care are used for secondary purposes such as clinical coding and subsequent statistical analysis. The current process of manual clinical coding is time-consuming, expensive and error prone. Computer-Assisted Coding (CAC) systems have the potential to expedite this process. We aim to develop a distributed knowledge-based computer-assisted clinical coding system that can semi-automate the process of clinical coding. This paper, describes the initial phase of our work which currently uses the pattern matching method to assign ICD-10-AM and ACHI codes.

Keywords. Computer-assisted coding, Natural language processing, Machine Learning, Distributed knowledge base.

Introduction An episode of care of a patient created at every visit to the hospital are written for various mnemonic and legal reasons (Dalianis 2018). Medical records include information such as sign and symptoms, diagnosis, interventions, drug treatment, and medication details. Medical records can be referred with different names such as patient record, clinical record, health record, case sheet, or case history, which are written by clinicians including physicians, nurses, radiologists, physiotherapists, and dieticians. Nurses usually take care of the patient on a daily basis and create clinical or progress notes, whereas the physicians make notes at certain time period points of care (Dalianis 2018).

Many hospitals are still using traditional method i.e., paper-based rather than electronic method for recording patient’s medical information. It is difficult to analyse paper-based medical records using computational linguistic methods, as the records need to be either scanned first and then use optical character recognition (OCR) techniques to convert into textual clinical narratives in structured or tabular form for computerized analysis (Dalianis 2018), (Kaur and Anupama Ginige 2018). Based on the medical records, a special set of codes referred to as International Classification of Diseases (ICD) codes are assigned by trained human experts, known as clinical coders. The current process of assigning clinical codes is a manual, which can be error-prone, time-consuming, and very expensive (Xie and Xing 2018).

A study (Farkas and Szarvas 2008) estimated that the cost incurred in assigning clinical codes and following up their corrections to be $25 billion per year in the U.S. A study (Arifoğlu et al. 2014), shows that the manual process of assigning clinical codes to patient records is erroneous. This study demonstrated that more than half of the records were assigned wrong ICD codes, when two auditors audited 491 pre-labelled patient records. The reasons behind the wrong assignment of clinical codes are due to the subjective nature of humans, inability to locate critical and subtle findings, fatigue, and limited expertise. To reduce coding errors and cost, various research studies


are being conducted to develop appropriate method for automated code assignment, referred to as Computer Assisted Coding (CAC) (Kaur and Ginige 2018). A literature review (Wang et al. 2018), found that out of 263 clinical studies, the majority of studies were conducted in the United Sates (n=236), followed by Canada (n=9), United Kingdom (n=5), Australia (n=3), and other countries. Moreover, the majority of the research based on auto-coding is focused on ICD-9-CM (Clinical Modification), ICD-10-CM and ICD-10-PCS which are U.S. classification systems, but very limited study is focused on ICD-10-AM (Australian Modification) and the Australian Classification of Health Intervention (ACHI) (Kaur and Anupama Ginige 2018).

Therefore, the aim of our work is to develop a distributed knowledge-based computer-assisted coding system that leverages on Natural Language Processing (NLP) and Machine Learning (ML) techniques, where a human coder will give a clinical narrative to the system and it will assist human coder with suggested codes based on the Australian classification system: ICD-10-AM and ACHI. Currently, we in the initial phase of development, where our system works on pattern matching method. This paper highlights the work done so far and the architecture design of the final system proposed.

1. Related Work

The history of CAC software goes back in the late 1990s and has evolved over the years in many ways becoming more robust, efficient and accurate (Schmidt). When CAC was first introduced to the industry, it brought a feeling of uncertainty among many coding professionals i.e., fear of losing job by replacing them (Miller 2018). One of the main goal of CAC was to reduce the amount of time that coders spend on reading the records. Ideally, CAC software facilitates in reading through and integrating free text from clinical notes, procedures, lab tests, and imaging reports. For many years, CAC used two basic methods for identifying ICD codes: Dictionary Matching and Pattern Matching. The issue with this matching system is that it may either generate extra codes which are not relevant to the patient care or it may miss certain issues or subtle findings that do not have specific keyword (Schmidt). Combining an NLP engine with CAC software can scan medical record, identify key terminology and suggest accurate codes for that particular diagnosis or treatment (Crawford 2013). An NLP engine consists of multiple components such as entity extraction, syntactic analysis, and semantic analysis, and each component performs specific operation in order to enhance the better understanding of text (Wolniewicz 2015).

By suggesting codes, CAC ensure that the human coders do not miss any subtle findings while skimming the records (Miller 2018). In earlier days, human coders were trained using coding books. There are various existing software such as TurboCoder1, 3MTM Codefinder2, and Encoder pro3, which are electronic version of coding books and assist clinical coders in finding ICD codes. Finding a single code in a book and coding a complete medical record using a book, are two different things (Miller 2018). There is a generation of human coders that have either been trained without CAC or have worked only using CAC. Though, CAC would increase the efficiency and accuracy of assigning codes, but would not replace humans. In fact, the human coder’s task will be of auditing by reviewing or editing the codes in case of any missing code or wrong assignment of code because while assigning codes, there are some coding guidelines and rules that must be

1 http://www.turbocoder.com.au/ 2 https://www.3m.com.au/ 3 https://www.encoderpro.com/epro/

http://www.turbocoder.com.au/

https://www.3m.com.au/

https://www.encoderpro.com/epro/


followed while assigning codes. Researchers are working on automation of clinical codes, but there is no such system or software that gives diagnosis or intervention codes without human interaction (Farkas and Szarvas 2008), (Kavuluru, Rios, and Lu 2015), (Kaur and Ginige 2018).

2. System Architecture

Our web-based system consists of front-end interface and functionality, connection between computers and a database in the back-end portion of the system. There are three layers which are taken into consideration:

1. User / External Layer

• Application / Logic Layer

• Data / Physical Layer

The user layer involves the connection of the computer which will then connect to the logical layer. When the user chooses what type of data is to be displayed onto the system, it will establish a connection to the database to output their desired result. The logical layer consists of a user login, user registration, medical records, clinical codes and display of clinical libraries. The physical layer involves the SQL query calling and displaying data from their respective databases, which would then be aligned towards the same data values.

3. Proposed method

Given in Figure 1, we aim to propose this system where the user will upload or enter an episode of care of a patient. The NLP engine will perform various tasks such as tokenisation, finding and replacing acronyms, synonym finding, spelling error detection and correction, part-of-speech (POS) tagging, named entity recognition, and negation detection to extract the meaningful information using various NLP tools, clinical Text Analysis Knowledge Extraction System (cTAKES), Systematized Nomenclature of Medicine- Clinical Terms (SNOMED-CT), and Unified Medical Language System (UMLS).


Figure 1. Flowchart of the proposed system.

After extracted meaningful information, the diseases and interventions will be tagged and highlighted with different colours. The next step is look up the tagged terms and suggest the ICD-10-AM and ACHI codes using ML engine. The ML engine will perform various tasks such as separate the diseases and interventions, chapter identification, apply coding rules (Australian Coding Standards) before suggesting codes to an episode of care of a patient, check whether the suggested codes are


valid 3, 4, or 5 digit codes by referring to Electronic Code Lists (ECLs), and assign ranking in terms of percentage (%age) based on predicted accuracy level. For example, the possibility of assigning 3 level code for term volume depletion is 99.78%. The human intervention and feedback section serves two main purposes: (1) The human coders verifies the codes manually by saying yes or no to the suggested codes and update the distributed knowledge based system and based on that feedback, the ML engine will be trained so that ML is capable enough to handle similar codes or combination of codes at an early stage. Moreover, if any code is missing or ML engine is unable to suggest, then the human coder can assign it manually. (2) Various human coders can work simultaneously and if they are unaware of any findings and assigned codes, then the human coders can share their findings with other human coders to seek help, suggestions and cross check their work before sending it to the distributed knowledge based system. The distributed knowledge based system will get updated regularly and also trains the ML engine with very possible combination of correct codes for future analysis.

4. Screen Design

We are currently under the development phase, but here are some screenshots which demonstrate the basic functionality of our system.

1. The login page provides the security for the users and implements user authentication. Oncethe user logs in using their credentials, then proceed to the main page and enter the medicalrecord number (MRN) of a patient.

2. Once the MRN is entered, then upload the clinical record to be processed. On the left handside of Figure 2, extracted keywords are highlighted. The “blue” colour highlighted keywordsrepresent diagnosis and “green” colour represent interventions or procedure information.Whereas on the right hand side, based on the highlighted keywords, their correspondingchapter details are given.

3. After expanding the chapter details, ICD-10-AM and ACHI codes are given for eachhighlighted keyword. Figure 2 and Figure 3 shows ICD-10-AM and ACHI code respectivelysuggested for every extracted keyword.


Figure 2. ICD-10-AM codes suggested for extracted keywords.

Once the user selects the appropriate list of codes, the information is submitted and stored in the database.

Figure 3. ACHI codes suggested for extracted keywords.


4. After submitting a record, the user can view the created record with all the findings and theircorresponding ICD-10-AM and ACHI codes as shown in Figure 4.

Figure 4. Complete record along with ICD-10-AM and ACHI codes.

5. Limitations of work

As the system is in development stage, there are areas to be improved. First, our system is currently using pattern matching method, where each text string is matched. If the exact match is found, then only our system predicts correct ICD-10-AM and ACHI code. Given in Figure 3, our system gives correct ACHI code for procedure “Administration of packed cells”, but also suggest lot of other codes which contain words administration, of, or cell. Similar is the case for diagnosis keyword named “Iron deficiency anaemia”. The correct code is D50.0, but is also suggesting extra ICD-10-AM codes for keyword Iron deficiency anaemia. To overcome this limitation, we will define some rules to avoid over-coding. Also, there are some invalid codes such as D50 and D51, which our system is suggesting can also be avoided by defining some rules. Second, many clinicians prefer to use abbreviations or synonyms while creating a clinical record. Therefore, to tackle with this problem, we will use SNOMED-CT for defining synonyms or acronyms.

6. Conclusion and Future Work

Clinical records have long been used for secondary use and are written by clinicians for various mnemonic and legal reasons. The current scenario of assigning clinical codes is a manual process which is a non-trivial task. There are various coding software available that identifies the key terminology and suggests their codes. Our work is in the initial phase of development, where it extracts the keyword(s) from the clinical record and assign ICD-10-AM/ ACHI codes based on the diagnosis and intervention details. In addition, our system is also able to predict which chapter the diagnosis or intervention belongs to. Though, our system is currently working on pattern matching approach, there are some limitations also. In future, as we will progress further into the development of this system, more functionalities will be added such as use of SNOMED-CT for defining synonyms and define rules according to ACS guidelines.


7. References

1. Arifoğlu, Damla, Onur Deniz, Kemal Aleçakır, and Meltem Yöndem. 2014. 'CodeMagic: semi-automatic assignment of ICD-10-AM codes to patient records.' in, Information Sciences andSystems 2014 (Springer).

2. Crawford, Mark. 2013. 'Truth about Computer-Assisted Coding: A Consultant, HIM Professional,and Vendor Weigh in on the Real CAC Impact', Jounal of American Health InformationManagement Association, 7: 24-27.

3. Dalianis, Hercules. 2018. Clinical Text Mining: Secondary Use of Electronic Patient Records(Springer).

4. Farkas, Richárd, and György Szarvas. 2008. 'Automatic construction of rule-based ICD-9-CMcoding systems', BMC Bioinformatics, 9: S10.

5. Kaur, Rajvir, and Jeewani Anupama Ginige. 2018. Comparative Analysis of AlgorithmicApproaches for Auto-Coding with ICD-10-AM and ACHI.

6. Kaur, Rajvir, and Jeewani Anupama Ginige. 2018. "Will Auto-Coding be a Reality AnytimeSoon?" In Proceedings of the HIMAA/ NCCH 35th National Conference, Health InformationManagement: Engaging the Next Generation, 25-33.

7. Kavuluru, Ramakanth, Anthony Rios, and Yuan Lu. 2015. 'An empirical evaluation of supervisedlearning approaches in assigning diagnosis codes to electronic medical records', ArtificialIntelligence in Medicine, 65: 155-66.

8. Miller, Elena. 2018. "Computer-Assisted Coding: Helpful or Hurtful?" In.: Journal of AHIMA.

9. Schmidt, Gregory. "ICD-10 Coding Using Machine Learning." Available from:http://www.gregoryschmidt.ca/writing/icd-coding.

10. Wang, Yanshan, Liwei Wang, Majid Rastegar-Mojarad, Sungrim Moon, Feichen Shen, NaveedAfzal, Sijia Liu, Yuqun Zeng, Saeed Mehrabi, Sunghwan Sohn, and Hongfang Liu. 2018. 'Clinicalinformation extraction applications: A literature review', Journal of Biomedical Informatics, 77: 34-49.

11. Wolniewicz, Richard. 2015. 'Computer-assisted coding and natural language processing'.

12. Xie, Pengtao, and Eric Xing. 2018. "A neural architecture for automated icd coding." InProceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume1: Long Papers), 1066-76.


Development of an interactive Messenger chatbot for medication and health supplement reminders Kerry Y. Fang1, Heidi Bjering1 1Western Sydney University, Campbelltown, Australia

Abstract.

Introduction: Non-adherence to medication and supplement regimens can negatively affect a patient’s health. Many of the adherence improvement strategies lack the personalised interactions needed to communicate with the patients, and such interaction can be crucial in the formation of trust and relationship, which can in turn positively affect adherence.

Aim: The aim of this paper is to discuss the use of chatbots, how Chatfuel works, the functions, benefits and limitations of our Messenger Reminder Bot, and the future trial planned to test our chatbot.

Method: Our solution to the non-adherence issues is to develop a Facebook Messenger Chatbot – Reminder Bot, using an online tool called Chatfuel, in order for us to utilise a chatbot to effectively communicate with the users through personalisation, motivation, and two-way interaction, with the ultimate goal of improving adherence and overall user satisfaction.

Results: The Reminder Bot had been developed and is currently being trialed with participants that take health supplements, and the results and feedback we have received so far are positive, where all participants showed improved adherence compared to the baseline. The majority of the participants liked the chatbot and it’s capabilities, such as the snooze button and the motivational AI scripts. It is anticipated that the final results of the trial with the Reminder Bot will show improvements in both adherence and user satisfaction, when compared to our previous trial on the use of an avatar-based reminder application.

Conclusion: Chatbots are becoming increasingly popular, especially in the service sector. There is the potential for our Reminder Bot to provide support and motivations to the users, ultimately improving their attitude towards adherence and overall user satisfaction.

Keywords. Chatbot, Chatfuel, Adherence, Patient-doctor communication

Introduction

Adherence to treatment regimens has been a long-term concern to healthcare professionals and the general health industry, as non-adherence behavior is linked to negative consequences to both the healthcare sector and the patients (Hughes 2004). Various strategies have been designed and developed to try and improved adherence towards treatment regimens, however most of them focused on simple reminder devices or the use of mobile phone based reminders like SMS and reminder apps. One major gap within these current reminder services and devices are the lack of personalisation, where information is designed tailor to each individual user, and such personalised


interaction can be crucial in motivating and improving user satisfaction, which in turn have the potential to improve the adherence. This paper discuss the development of a Facebook Messenger Chatbot Reminder that uses two-way interactive communication in order to provide motivation and encouragement to the user, which is expected to lead to a better adherence towards treatment regimens.

1. Background1.1 Adherence vs Non-Adherence

Medication non-adherence is considered as one of the major causes of morbidity, mortality and health care costs (Yeaw et al. 2009). Several factors can influence a person’s adherence behavior and decision. Jimmy and Jose (2011) stated that most common reasons for non-adherence include fear of side effects, lack of health knowledge and beliefs about the medications and illnesses, lack of trusting patient-doctor relationship, and having complex regimens. Study has shown that patients with chronic diseases only take about 50% of prescribed medications (Haynes et al. 2002).

Users that take health supplements also faces the problems of low adherence rate. Many people nowadays seek natural treatment options, and as a result some patients will opt to use health supplements as an alternative to medication to treat or prevent certain illnesses (Timbo et al. 2006). James (2017) mentioned it is estimated that more than 60% of Australians uses some type of supplements. Most health supplements and prescribed medications have similar dosage/schedule (e.g. once a day, twice per day) and taking behavior (e.g. before meal, after meal). Non-adherence to either one can resulted in health-related consequences. Even though various interventions have been built to try and improve adherence (Henriques et al 2012; Hardy et al, 2011), majority of the interventions lack the personalised interactive communication that can be important in educating, motivating and encouraging patients to adhere to their medication/supplement regimens. An Artificial Intelligent agent such as a chatbot has the potential to create such personalised interactivity through two-way communication.

1.2 Chatbot 1.2.1 What is a chatbot?

Chung and Park (2018) defined chatbot as “a natural interactive simulation system with AI-based conversational modeling and knowledge”. It can interact with users through an interface such as Facebook Messenger. Unlike some mobile apps, chatbots are easy to use and requires little to no familiarity (Jumaah 2018), and this can be especially beneficial to elderly users or those that are not very tech-savvy. Wang and Siau (2018) stated that based on market research done by Grand View Research, it is estimated that globally, the chatbot market will reach $1.23 billion by 2025, indicating chatbot’s popularity and growth potential.


1.2.2 Use in healthcare and other industries

Chatbots have been successfully implemented in healthcare and other industries like banking, retail, education, airline and many more (Abhishek 2018). Baidu developed a virtual medical chatbot “Melody” within the Baidu Doctor app which enables patients to ask questions, make appointments or get health-related information (Chung & Park 2018). Another chatbot named Oscar, which interacts with users via Facebook Messenger, offers health-insurance estimation to its users (Chung & Park 2018). Brandtzaeg and Følstad (2017) pointed out that one major reason for users to use chatbots is productivity, which means users can get quicker answer with less effort. They further stated that the human-like communication provided by chatbots give users the opportunity to ask questions, and get meaningful answers in return, and such interactive and personal response is what makes chatbot popular amongst the different industries.

2. Methodology 2.1 Aim of our Messenger reminder chatbot – Reminder Bot

In order to help improve some of the adherence issues mentioned above, we have developed a chatbot to be used on Facebook Messenger – the Reminder Bot. The decision to use a Messenger chatbot is based on the feedback we have gathered from an initial trial with a simple avatar-based reminder app [10], where participants in the intervention group were given an iPad with the avatar-based reminder app installed to use for the duration of the trial. It was found that the majority of the participants enjoyed the communication and information provided by the avatar, however most of them stated that they would prefer a text-based reminder and the ability to use such reminder technology on their own mobile device.

The aims of our Reminder Bot are to try and improve user’s adherence and overall satisfaction through two-way communication. In addition to the personalised reminders, the chatbot will provide health knowledge, motivation and encouragement to the users to try and achieve the goals.

2.2 Functions of Reminder Bot

Our Reminder Bot provides various functions that help create a personalised interaction, which can result in the formation of a relationship with the users. In the health sector, it is said that effective patient-doctor relationship is important because it can lead to better knowledge and adherence (Henriques et al. 2012). One study found that adherence is generally good when doctors are emotionally supportive, provide patients reassurance and respect, and treating the patients as equal partner (Moore et al. 2004). In order to imitate healthcare professionals through the use of an AI agent, like a chatbot, certain characteristics need to be incorporated into the agent. Characteristics such as attentive, knowledgeable, interactive, motivative, convincing, friendly, supportive, personalised, with the use of simple language, are some of the important aspects an AI agent need to integrate to effectively simulate healthcare professionals [9]. The Reminder Bot incorporates many of these characteristics in order to try and simulate a healthcare professional or a carer when giving health-related answers and advice, with the goals of improving adherence and satisfaction. Some of the functions incorporated into our chatbot include:


• Health-related questions and answers/conversations: The Reminder Bot can answer somehealth-based questions by giving feedbacks and advice based on available resources online,for example WebMD (WebMD 2019). The knowledge for the script and how the answerswere structured was done by researching articles on patient-doctor communication.

• Health coaching: Users are given the option to subscribe to the daily health tips when theyfirst interact with the chatbot. Once subscribed, they will receive a health-related tip everyday, in either text and image, or video form.

• Personalised reminders: The Reminder Bot can give personalised reminders to individualusers, providing them with details such as dosage information and pictures of themedication/supplements they take. There is also a snooze option to allow the users to snoozethe reminders for 5 or 10 minutes.

• Personalised information about the supplements/medications are provided to each user bygathering information from online resources e.g. WebMD, in order to present users with moreknowledge (i.e. its use and possible side effects).

2.3 Development of Reminder Bot

The Reminder Bot was built using an online tool called Chatfuel. Chatfuel provides an online platform that allow users to build chatbot for Facebook Messenger via an easy-to-use visual interface, which enable non-technical users to design and build chatbot message flows and conversational rules (Chatfuel 2018). Chatfuel is the largest platform for building Facebook Messenger chatbots, powering about 46% of all chatbots currently on Facebook Messenger. Companies like Adidas, BuzzFeed, National Geographic and ABC all uses Chatfuel to design their Messenger chatbot (Chatfuel 2018).

The Chatfuel dashboard enable developers to select and edit chatbot using their easy-to-use and navigate interface. Some important and useful components include Cards, Blocks, Attributes, Broadcasts, Sequences, and AI messages. Luca (2018) stated that Cards are one of the most basic building component of a chatbot, and they allow developers to add text, images, videos and many other components to the chatbot’s response. Cards also enable developers to link chatbot responses to other existing plugins available, e.g. Google Search, RSS Import, Youtube, Instagram etc, or it can allow them to redirect the conversation to Sequences and Blocks they’ve created before. Lucas also pointed out that Blocks are containers for one or more Cards, and when a Block is triggered by users interacting with the chatbot, that user will receive all the Cards contained in that Block. Attributes provides personalisation and advanced conversational flows in a chatbot through user filtering. Developers can use existing system attributes or create their own custom attributes in order to filter user. (Luca 2018). Edgar (2018) stated that Broadcasting function allow developers to send messages to users. Messages can be set to send now, scheduled later, or send automatically within Sequences. Sequences are follow-up messages that the chatbot sends to the users when they interact with the chatbot in a certain way, i.e by subscribing to the sequence (Edgar 2018). The AI message function is what’s unique and useful in distinguishing one chatbot from another. The AI component will help chatbot understand the messages user sent (Edgar 2018). Figure 1 shows an overview architecture of our Reminder Bot.


Figure 1. Reminder Bot Architecture

The health coaching function of Reminder Bot was built using a combination of Blocks, Cards, Sequence and Subscription components. Multiple Cards containing health-related tips, video and information were created within different Blocks and placed under “Health Tips” Sequence. Therefore when a user subscribe to the “Health Tips” Sequence, they will receive a health-related tips or video everyday by triggering the corresponding Blocks within the Sequence.

The Health Q&As/conversations and Personalised Supplement/Medication Information were built using Cards, Blocks, AI messages and Attributes components. The personalised information and responses were triggered by filtering users based on different attributes, e.g. when a user wants to know more information about the supplements he/she takes, we will need to filter the user based on the stored “SupplementName” attribute, and then send the relevant supplement-related information to the user. Possible keywords and phrases that should trigger a specific response from the chatbot were entered into the AI message section on Chatfuel dashboard, then next to those keywords/phrases we entered the chatbot’s response(s) to that specific keyword(s)/phrase(s) in the form of text or re-direct to a Block (Luca 2018). The responses were designed by researching articles and resources on patient-doctor communication, online medication/supplement information, as well as existing chatbots. The phrases entered do not have to exactly match the user’s message, e.g. we set a phrase “my supplement information”, and the AI will also trigger that rule and the corresponding response if the user asks “Can you tell me information about my supplement?”. We have also incorporated Dialogflow and Janis.ai into Chatfuel as they provide a more sophisticated AI support and therefore can further enhance the conversational aspect of the Reminder Bot. Dialogflow is powered by Google, and it uses natural language processing and machine learning to understand not only the keywords, but the meaning of words inside of phrases (Dialogflow 2019), it is therefore smarter than Chatfuel’s own AI function. Janis.ai is the AI assistant for bots, it works by connecting and integrating Google’s Dialogflow to Chatfuel (Janis 2019).

Personalised reminders were designed using the Cards, Blocks, Attribute and Broadcast components. Reminders are send using Broadcast messages, and can be set to send now or scheduled for later. When scheduled for later we can choose the date/time and how often (every day, every month etc) the message is to be sent. We can also send broadcast messages by filtering users based on different user attributes, e.g Broadcast a personalised message daily at 7pm to users whose ReminderFrequency attribute is “daily”, and ReminderTime attribute is “7pm”.


The personalised message can be “Hi {{FirstName}}, it is now 7pm, time to take your {{SupplementName}}”, where {{ }} are used to retrieve and display user’s data stored in the attributes. We can also incorporate Cards and Blocks into the broadcast messages, e.g. we create a Block called “Taken Supplement”, which is linked to a button that is send every time a reminder is broadcasted, users click this button to indicate they have taken the supplement for that reminder session. Within this “Taken Supplement” Block we will have various text, image/GIF Cards that let users know they’ve done a good job adhering to their regimen.

2.4 Benefits and limitations of Reminder Bot The main benefit of the Reminder Bot is its ability to provide interactive personalised communication, and the results from such personalised interaction are often increasing trust, improved knowledge, and the formation of a relationship, which can potentially lead to better adherence and user satisfaction. Running it through Facebook Messenger makes it portable, easy to install, learn and use. Facebook Reports on Investor Relations (2019) showed that worldwide, there are over 2.32 billion monthly active Facebook users as of 31st December 2018.

Despite the benefits, there are still limitations that exists with the Reminder Bot. Firstly the users will require a mobile device that supports the Facebook Messenger app and it needs to have either Wi-Fi connection or cellular data network i.e. 3G or 4G in order for the chatbot to work. Secondly the users will need to register for an account on either Facebook or Messenger. Thirdly depending on the type of smartphone used, there can be a lack of support for verbal/spoken information, and this can create problems to those that are vision impaired. However most of the latest mobile devices has built-in text-to-speech function, so users can use this function to verbalise the conversations. Moreover, with the increasing use and popularity of smartphones, Facebook and Internet connections, these limitations should only affect a small percentage of people worldwide.

3. Preliminary ResultsA trial is currently underway to evaluate our Reminder Bot with users that take health supplements, in order to test the efficacy of our chatbot in a low risk intervention. The results and feedback we have gathered so far from the participants showed that the use of an interactive chatbot can positively affect adherence. All participants so far showed improved adherence when compared to their baseline adherence rate (Table 1).


Participant Baseline adherence rate

3-week mean adherence rate

A 83.85% 96.41%

B 8.04% 93.65%

C 71.87% 90.62%

D 95.24% 100%

E 71.43% 95.24%

F 14.29% 76.19%

G 65.71% 88.57%

H 90.48% 93.65%

I 85.71% 95.24%

J 71.43% 100%

Table 1. Preliminary Results (Adherence Rates) The majority of the participants found the various functions and capabilities, such as the personalized reminders, chatbot responses and quotes, and the images/GIFs, to be interesting, helpful and motivational. All of the participants found the chatbot to be easy to use and very convenient as all of the participants have Facebook and Messenger installed on their phone, and the majority of them have their phone with them most of the times. On the other hand, about 40% of the participants so far reported that the daily health tips subscription can be annoying at times. The participants were informed at the start of the trial that they can turn off the subscription at any time by simply texting “unsubscribe me” to the chatbot, however most of the participants noted that they have forgot how to do this, so for future revision we will be including a note within the daily health tips with directions on how to unsubscribe.

The results we have gathered so far shows that there is a higher user satisfaction and better user feedback with the Reminder Bot than when compared to the results we have gathered from our Simple avatar-based reminder application in our initial trial [10].

4. Conclusion/Future work In conclusion, chatbots are becoming increasingly popular and are widely used by many businesses and industries. Our Facebook Messenger Reminder Bot can potentially help users to better adhere to their medication/supplement regimens through providing personalised interactions.

So far the preliminary results from this chatbot trial has shown improvements in both adherence and user satisfaction when compared to the baseline adherence rate, as well as when compared our earlier simple avatar-based reminder app [10]. The chatbot trial is currently in process, but we are hoping that the final results can form the basis of further testing with users that take prescribed medications.


5. References [1] Abhishek, S. (2018), ‘Top chatbot use cases in different industries’, Available at:

https://botcore.ai/blog/top-chatbot-use-cases-in-different-industries/

[2] Brandtzaeg, P.B. and Følstad, A. (2017) ‘Why people use chatbots’, In International Conference on Internet Science (pp. 377-392). Springer, Cham.

[3] Chatfuel (2018) Chatfuel. Available at: https://chatfuel.com/

[4] Chung, K., & Park, R. C. (2018), ‘Chatbot-based healthcare service with a knowledge base for cloud computing,’ Cluster Computing, 1-13

[5] Dialogflow (2019) Dialogflow. Available at: https://dialogflow.com

[6] Edgar (2018) Broadcasting in Chatfuel. Available at: https://docs.chatfuel.com/broadcasting/broadcasting-documentation/broadcasting

[7] Edgar (2018) Sequences in Chatfuel. Available at: https://docs.chatfuel.com/broadcasting/broadcasting-documentation/sequences

[8] Edgar (2018) AI Setup in Chatfuel. Available at: https://docs.chatfuel.com/ai/ai-documentation/ai-setup

[9] Fang, K. Y., Bjering, H., & Ginige, A. (2017) ‘Simulating Human Carer with an Avatar to Improve Medication Adherence’, In Integrating and Connecting Care: Selected Papers from the 25th Australian National Health Informatics Conference (HIC 2017), 6-9 August, 2017, Brisbane, Qld. (pp. 35-40)

[10] Fang, K. Y., Bjering, H., & Ginige, A. (2018) ‘Adherence, Avatars and Where to From Here’, Studies in health technology and informatics, 252, 45-50.

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[15] James, B. (2017) ‘A closer look at Australia’s most popular supplements’, ABC News, Available at: https://www.abc.net.au/news/health/2017-02-13/a-closer-look-at-australias-most-popular-supplements/8265840

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Professional practice abstracts


Maintaining Data Quality in a Large Patient Administration System, what's it worth? Karen Baker1 1Mater Group, South Brisbane, Australia

Introduction Mater Group’s patient administration system (PAS), i.Patient Manager (iPM), holds over a 1.5 million records for seven (7) facilities and has been in place for over 10 years. This presentation will describe a current initiative to improve and sustain the quality of data in the system.

As Health Information Managers know, the PAS – incorporating the Patient Master Index, as it does – is a core component of all our systems, whether we have paper-based health records or a network of electronic information systems. At Mater, of the hundreds of our active systems, iPM is considered to be “the source of truth” for patient-related data and with so many other downstream systems relying upon it, safeguarding it is an important but very demanding task.

Current State Mater Group uses iPM as the Patient Administration System. Patients are identified with a unique Unit Record Number (URN) which is assigned at registration in iPM. This URN is used by all patient based systems across Mater Group.

iPM is principally used to record administrative data for all Mater patients/clients. It has a large number of modules that include demographics, referrals, scheduling, attendance, billing, record tracking, occupancy, alerts, and many more.

Although all staff contributing to iPM must first have had the necessary training in the system, and while there is a program of systematic checks and audits in place, with so many patients, so many people involved, with so much data, there are still many ways that errors and omissions may be made.

Anecdotally, we in Information Management heard about and also observed instances of reduced data quality.

The Initiative In early 2019, after conducting a brainstorming session with representatives from a wide range of stakeholders from across the organisation and compiling the raised issues into themes, the Director of Information Management proposed that several working groups be created, each of which would review a key aspect of the system to identify any data quality issues, quantify them, uncover their causes, devise ways to eliminate or mitigate them, and come up with an implementation plan to put them into action. The six nominated target areas were:


• Admissions, Discharges and Transfers

• Alerts Management

• Clinics

• Patient Declaration and Consent

• Contracts

• Patient Registration and GP Details

Participation in the working groups was by self-selection and also by nomination and representation was broad but with the majority from Administrative Services and Information Management.

The Tool – A3 Thinking Process As described by Matthews (2010), the A3 is a problem solving tool that is conducted to show the path to improvement, set out on an A3-sized piece of paper and categorising the issue into:

• Problem situation

• Target condition

• Cause Analysis/Diagnostics

• Countermeasures

• Implementation/Plan

• Follow-upIn Matthews’ view there is great benefit to be gained from applying the A3 process in an organisation to promote improvements and a problem solving environment.

The Mater Improvement Framework has been developed to support a culture of encouraging improvement – where it is “everyone’s job to do their work and improve their work” aligned with best practice. Mater has been proactive in teaching the A3 process to staff throughout the organisation. This is carried out over two half-day A3 Thinking Workshops, offered and promoted by the Quality by Design Team which issues certificates of attainment at the completion of the course.

Towards the New State The A3 process is a systematic process that requires the right people with the right knowledge to have the right amount of involvement. A casual approach will not lead to success, however, commitment, collaboration and communication will.

Done properly, this is a large undertaking which will take time and concerted effort to achieve and maintain the target condition across the whole PAS, but the presentation will also include details of objectives achieved and specific lessons learned so far. The whole process is based on a continuous cycle of Plan-Do-Check-Act (PDCA) (MindTools) and we must accept that if any gains are to last.


References Matthews, DD 2010, The A3 Workbook: unlock your problem-solving mind Taylor and Francis Group Boca Raton, London, New York, viewed 07 June, 2019, https://scholar.google.com.au/scholar?cites=4299446438520218877&as_sdt=2005&sciodt=0,5&hl=en

Plan-Do-Check-Act (PDCA), Continually Improving, in a Methodical Way, MindTools, viewed 07 June 2019, https://www.mindtools.com/pages/article/newPPM_89.htm


Discovering the potential of Australia's first lifelong person-centric health data set Vicki Bennett1, Navreet Bhattal1, Jaclyn Chan1 1Australian Institute of Health and Welfare, Bruce, Australia

Introduction The My Health Record (MHR) is the first national person-centred digital health record in Australia. The Australian Institute of Health and Welfare (AIHW) will become the Data Custodian for the secondary use of MHR system data once amendments to the My Health Record Act 2012 commence in late 2019.

The Framework to guide the secondary use of MHR system data (the Framework) released in May 2018 outlines how data in the MHR system may be used for research and public health purposes while preserving the privacy of individuals and security of the system (DoH, 2018).

In July 2018, the AIHW was tasked by the Department of Health and the Australian Digital Health Agency to support the implementation of the Framework. The first release of data will occur following the establishment of the Framework’s governance arrangements and the necessary technical infrastructure.

Using MHR system data for secondary purposes will open a range of possibilities to fill data gaps and allow the health sector to better understand how Australia’s health system is used, as well as gaining valuable insights into health outcomes for patients.

Professional practice/case study description To enable us to undertake our role as Data Custodian of the MHR system data, AIHW is examining the content of the data in the MHR system to ensure fit for purpose data of sufficient quality can be made available, and that researchers and other data users are well supported in their data requests. The role of the MHR Data Unit at the AIHW is to develop a good understanding of the data held within the system.

As at 31 January 2019 after the end of the ‘opt-out’ period, the participation rate for the MHR was 90.1 per cent, with a national opt-out rate of 9.9 per cent. There were over 15,560 health care provider organisations registered to use the system, including general practitioners, pharmacies, pathology and diagnostic imaging services.

The MHR system is composed of many types of documents, such as shared health summaries, discharge summaries, event summaries, pathology reports, prescription records, specialist letters, eReferrals and Medicare documents.

Implementation/experiences The quality and coverage of the data from the MHR system must be explored to understand the role it could play in supporting secondary use purposes. AIHW has developed a data assessment plan, which applies standardized, validated methodologies to establish a common understanding of the


strengths and limitations of MHR system data. This plan has been structured intro three stages – discovery, development and implementation which is being undertaken over 2 year period.

As part of the discovery stage, a MHR clinical document data dictionary is being developed to accurately describe and define the data collection. Additionally, an environmental scan is being undertaken to identify global best-practice methods for data quality assessments to ensure an evidence-based approach is taken to assessing the MHR data. This will also consider the types of data requests, potential use-cases and research questions that have been made of other similar electronic health records, for example in the UK.

To reliably use this data, researchers need to understand the quality of the data. To assist data users, a Data Quality Statement will also be developed. The approach that is being used considers the different data quality dimensions that will provide a comprehensive understanding of whether MHR system data is ‘fit’ for specific secondary uses, and is valid and generalizable (Weiskopf & Weng, 2013). The AIHW’s Data Quality Management Framework being used for this work identifies five domains when considering quality including, timeliness, accessibility, interpretability, relevance, accuracy and coherence (AIHW, 2011). It will be important to develop a full understanding of the interrelationships and data characteristics as they relate to data quality.

The team currently working on undertaking this work is comprised of two HIMs and one Health Informatician (HI) who are bringing our specialist knowledge and skills to this important piece of work. The HIM/HI skill sets within the team have allowed us to not only understand the issues that need to be considered when assessing electronic health record content, but also how to consider when and how data may be fit for certain uses.

Conclusion The MHR system is an enduring data asset that can help guide health monitoring and performance reporting, policy development, health services planning and research. There are expectations that this new data asset will answer questions that cannot be explored using existing data sources. As further understanding of the MHR system data is developed, these lessons will be shared.

As the MHR systems grows and expands it will be necessary to continuously assess the content to ensure it meets the needs of users. The knowledge and skill unique to the HIM/HI professions are invaluable in managing and guiding the use of this new and extremely valuable national asset, so it would be good if more HIM/HI practitioners could be involved into the future.

References DoH (Department of Health) 2018. Framework to guide the secondary use of My Health Record system data. Viewed 6 February 2019, http://www.health.gov.au/internet/main/publishing.nsf/Content/eHealth-framework.

Data Quality Statements (DQS) policy and guidelines 2011. Accessed 15 February 2019, http://bruce.aihw.gov.au/Stats/AIHW%20statistics%20guide/Data%20Quality%20Statements.aspx

Weiskopf NG, Weng C (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research, J Am Med Inform Assoc. Jan 1;20(1):144-51 https://www.ncbi.nlm.nih.gov/pubmed/22733976


Where on Earth is Tuvalu? My Year as a Volunteer HIM in a Developing Country Kaye Borgelt1 1Australian Volunteers International, Netherby, Australia

1. Introduction What does an experienced Health Information Manager do when they leave the Victorian Public Health Sector after thirty years? I packed up my knowledge and suitcase and relocated to Tuvalu, one of the smallest and least visited countries in the World, to work for twelve months as a Volunteer Health Information Manager with Australian Volunteers International, an aid program funded by the Australian Government.

Tuvalu, which comprises nine small tropical islands, has a total population of just 11,000 and only one hospital located on the island of Funafuti. My role as Health Information Manager was designed to assist in improving medical record documentation and building capacity and knowledge for local staff. It proved to be so much more.

2. Back to Basics - Using Health Information to Improve Health Outcomes Health Information Managers have a range of skills that can assist in a meaningful way to improve health outcomes, a fact sometimes forgotten as we struggle to maximise casemix based funding outputs in the modern and complex Australian health care system.

The tiny country of Tuvalu has a truly universal health care system with all services, from paracetamol pain relief to kidney transplants undertaken in the faraway countries of Malaysia and India, provided free to all citizens.

As the first and only Health Information Manager to spend an extended period of time in Tuvalu, my role included many firsts. Hopefully in providing the following services, my work has promoted sustainable change and ultimately improved the health outcomes of a population who continue to celebrate a baby’s first birthday as a major milestone, and whose average life expectancy is less than 65 years:

• Introduction of inpatient admission, discharge and transfer process;

• Introduction of morbidity coding;

• Review and revision of Medical Cause of Death Certificates;

• Implementation of National Guideline on completion of Cause of Death Certificates;

• Introduction of in country mortality coding;

• First ever report to Tuvalu Government detailing mortality trends over the previous five years and highlighting emerging public health issues;

• Introduction of quarterly inpatient and outpatient statistical reporting;


• Analysis and reporting of overseas referral scheme, incorporating clinical and financialinformation; and

• Tuberculosis, Rheumatic Heart Disease, Leprosy, Dengue Fever and other public healthcrises.

Over the course of the twelve-month journey I learnt many valuable lessons:

• Be prepared for no real systems or processes to be in place;

• Understand that everyone is doing the best they can working with extremely limitedresources and capacity;

• Have conversations with local staff to ascertain what processes are already in place,what works well, what does not work, what they enjoy about their role and how it fits inwith how the hospital works;

• Consider the most important and fundamental systems and work at implementing them– MR Numbers, Admitting Patients, Labels, Armbands, Morbidity and Mortality coding;

• Be able to justify why implementing these new systems is so important and how it willimprove patient safety and/or quality of care;

• Be realistic about what is achievable and ensure anything you change is sustainableafter you have left the country;

• Be flexible and understand that what you actually do may not exactly match the missionobjective;

• It is a sign of trust when people start asking for assistance so never say ‘no’ if it is evenremotely within your area of expertise;

• Listen, listen and then listen some more;

• Keep smiling and top up your reserves of resilience – you will need it;

• Pack a copy of Edna K. Huffman textbook, Medical Record Management, it has all theanswers about medical records in the pre-digital age;

• For the first month just put one foot in front of the other and do not make any judgementabout whether you are enjoying the experience – it takes time to adjust to such adifferent culture/way of life;

• Learn to embrace and enjoy cold showers.

From the sublime to the ridiculous my year in Tuvalu was a truly amazing experience. Volunteering is a great way to ‘pay it forward’ and opened up an entirely new and wonderful world for which I will always be grateful. As one of the most at-risk countries in regard to climate change and increasing sea levels I can only echo the cries of Tuvaluans when I say “Save Tuvalu to Save the World”.


“Sharing is Caring” – A Regional EMR Claire Bridson1 1Ballarat Health Services, Ballarat, Australia

Introduction Grampians Rural Health Alliance (GRHA) in partnership with its lead agency, Ballarat Health Service (BHS), expanded the existing clinical information system (BOSSnet) in use at BHS, to an additional nine (9) health services within the Grampians region. Geographically this covered approximately 48,000 square km from Ballarat to the South Australian border, servicing 250,000 people. The project’s aim was to achieve an integrated, shared Electronic Medical Record (EMR), both within multi-campus agencies and more broadly between health services within the Grampians region.

1. Implementing a Regional EMR 1.1 Background The project was funded by the Department of Health and Human Services (DHHS) and in kind funding from each of the participating health services. A centralised project team was recruited to oversee the project and this project team worked in conjunction with local project staff. The project teams were supported by the Project Governance and Project Managers Groups which had representation from all agencies. Clinical engagement was sought at each agency as required and targeted to specific components of the project deliverables.

Although the clinical information system had been in place at BHS since 2009, the key project challenges, related to the complexities of creating a shared technical/business platform, which effectively supported multiagency use of a single database. This required preserving each agency’s discrete record to meet the legal requirements of the organisations.

1.2 Infrastructure and Integrations From a technical viewpoint, a total infrastructure upgrade was completed. There were also a number of application changes required to accommodate the unique requirements of the project, including a shared or virtually merged view of a patient record, where records exist for an individual patient at multiple different agencies.

Interfacing requirements to support the project were significant with the integration of nine Patient Administration Systems, whilst retaining total separation of patient numbering and episode data to the source agency. This allowed for the potential of the same Unit Record Number (URN) to be in use at different agencies for different patients. In addition, Pathology and Radiology Interfacing from four different providers, was implemented, with data for multiple agencies coming into the system through the same interface.

.


2. Project ApproachThe central GRHA project team worked with each agency to understand their medical record workflows, develop the resources required for the system configuration, implementation, end user training and system testing.

They also worked with clinical groups across the region to develop regionally agreed templates for electronic documentation in the outpatient/community and inpatient setting.

With multiple organisations using the same database, it was imperative to reach region wide agreement on several key issues, such as naming conventions, key performance indicators related to scanning, behaviour of the shared record, and a common patient data privacy policy. The collaboration required to achieve this should not to be underestimated and was a key factor in the success of the project.

3. ConclusionsThere is now a shared electronic medical record in the Grampians Region. Clinical staff have access to patient medical record data, including radiology and pathology results and patient alerts across all ten agencies. Individual Health Identifier (IHI) polling is enabled for all agencies within BOSSnet and this is the cornerstone for the virtually merged record, as well as allowing access to the My Health Record. A new electronic Discharge Summary, based on the new national guidelines is also in the final stages of testing, with the capability to upload to the My Health Record.

This project was unique. The implementation was across a large geographic region comprising 10 separate health services with 28 disparate facilities, working collaboratively to implement a single shared solution. Benefits of this project are already being realized, including better access to clinical information leading to improved clinical outcomes for patients and the ability to share coding resources around the region, to name just a few. There is now a strong foundation and structure to enable ongoing collaborative work on shared patient centric regional goals.


Relative Stay Index Version 2.0 – An improved measure of length of stay. Rohan Cattell1, Alix Higgins1 1Potential (x) and Health Roundtable, Surry Hills, Australia

Introduction As healthcare moves towards an increasingly digital delivery, the exponential growth in data has facilitated advances in performance metrics. However, when handling healthcare data, the use of crude measures is problematic, due to the number of complicating factors (Kahlert et al 2017).

Over the past 20 years, The Relative Stay Index (RSI), developed by the Health Roundtable (HRT), has been used as a quality benchmarking indicator for hospitals, taking into account the risk factors that can influence length of stay. The RSI is designed “to get managers and clinicians to discuss operational improvements in length of stay for large cohorts of patients” (Cattell 2018).

As health information becomes more detailed and accurate, healthcare indicators must be updated to ensure applicability and relevance. To this end, The Health Roundtable’s RSI has been redeveloped, incorporating new approaches to the calculations, and refining the factors that are known to impact length of stay. Health information managers, in their integral role as the custodians of healthcare information, are well placed to be able to take part and contribute to the conversation that will be generated through the update of RSI across the 177 member hospitals in Australia and New Zealand.

1. RSIThe Relative Stay Index (RSI) is an indicator of whether a hospital's length of stay (LOS) is different from other hospitals after adjustment for casemix and demographic attributes of the patient. The RSI can be used to signal where variations in LOS may be occurring, which may warrant further investigation.

1.1 RSI version 1.0

The RSI is calculated for a cohort of patients as 𝑅𝑅𝑅𝑅𝑅𝑅 = 𝐿𝐿𝐿𝐿𝐿𝐿𝐸𝐸𝐿𝐿𝐿𝐿𝐿𝐿

.

The expected length of stay (ELOS) is determined using the episode’s DRG, care type, urgency status, transfer status, separation mode and age. It is usually expressed as a percentage, so an RSI over 100% indicates a longer aggregate length of stay than expected and below 100% indicates a shorter aggregate length of stay than expected, with reference to the particular variables we have adjusted on and the particular dataset that we used to train the model. The resulting RSI is then used to track performance and identify areas for improvement.

The original ELOS calculation was developed over 20 years ago. Although it continues to provide a quality and robust result, like all good things, it has been updated to incorporate new approaches to healthcare statistics


1.2 RSI version 2.0 The new updated version of RSI has utilised a type of regression model called a generalized additive model (GAM). This approach means that the complexity of an episode and the age of the patient can both be incorporated as continuous predictors (instead of categorical components), which can then be broken down by subgroup.

This key difference ensures that the benefits of the complexity model introduced to Australian Related Diagnosis Related Groups Version 8 are realized to the full capacity. Additionally other changes, such as new exclusions and adjustments to the model, support the delivery of a more finely adjusted indicator.

2. Practical implicationsThe use of ECCS as a key RSI variable has implications for health information managers, particularly in developing an understanding of the role of ECCS in the risk adjustment, and to contribute to the development of action plans underpinned by RSI results.

3. ConclusionAs should be expected with a change to the mathematical model, a consequence is a change in the RSI results. However this relatively minor disturbance delivers a model that allows for greater flexibility and better risk adjustment, especially when looking at detailed DRG families.

To date, the feedback from members has been positive and has, as originally intended, driven the conversation about the delivery of healthcare. Health information managers have a key opportunity, as the primary custodians of health information, to join this conversation and contribute valuable knowledge and experience that will assist with improving the delivery of healthcare.

References Cattell, R 2018 Relative Stay Index (RSI) version 2.0, viewed 30 May 2019 https://www.healthroundtable.org/GetNews/NewsArticle/tabid/1457/itemid/380/amid/5205/default.aspx

Kahlert, J, Gribsholt, SB, Gammelager, H, Dekkers, OM & Luta, G 2017, ‘Control of confounding in the analysis phase--an overview for clinicians’, Clinical Epidemiology, p. 195, viewed 30 May 2019, <http://search.ebscohost.com/login.aspx?direct=true&db=edsgih&AN=edsgcl.531216867&site=eds-live>.


Is the health information profession an aging workforce: insights from Australia’s first national workforce census Kristina Donovan1, Kerryn Butler-Henderson2 1Western Health, Footscray, Australia, 2University of Tasmania, Launceston, Australia.

1. IntroductionThere is growing concern that the combination of the growth of the health information industry and the aging workforce could lead to both a labour and skills shortage (Health Information Management Association Australia, 2016). However, there is limited information about the Health Information Workforce (HIW), with only a small number of studies on the workforce, all greater than five years old (Australian Institute of Health and Welfare, 2010, Legg & Lovelock, 2009, Health Workforce Australia, 2013). The HIW is defined as “a role where the primary function is related to developing, maintaining, or governing the systems for the management of health data, health information, or health knowledge” (Butler-Henderson & Gray, 2018).

2. MethodSecondary de-identified data from the inaugural Australian HIW Census conducted in May 2018 was analysed using descriptive and inferential statistics. Normality testing of age was undertaken, as well as investigation of average age with respect to occupation, employment status, paid weekly hours and highest qualification. The analysis defined an aging workforce as one where over half of the workforce is aged 45 years or older (Brooke 2003). The first summary report estimates the 2018 Census obtained a 20% response rate (Butler-Henderson & Gray, 2018).

3. Results and discussionAll 1596 responses from the 2018 Census were included in this analysis. The investigation found that the workforce is predominantly female, with a negatively skewed distribution towards an older workforce. The average age is 46.27 years (range 21-73, Standard Distribution [SD] 34.76-57.79). This indicates the majority of the workforce is an older workforce, with 56.14% (SD 49.69-61.12) aged 45 years or older. A major concern of having an aging workforce is the likelihood of reduced participation and decreased productivity resulting in reduced hours worked and slower economic growth (Commonwealth of Australia, 2015). The census data reflected this, with the average age of those working 15 hours or less as 51.82 years (range 21-73, SD 39.86-63.78).

Interestingly, the average age varied significantly between qualifications achieved by the workforce. Older cohorts were observed those who reported their highest health information qualification was a vocational course (average 50.94 years, SD 39.72-62.15), honours or graduate certificate/postgraduate diploma (average 48.95 years, SD 37.97-59.92) or a doctorate (average 51.60, SD 40.47-62.73). However, there was a younger average for those who reported a Bachelor degree (average 44.63 years, SD 33.88-55.38) or a Masters degree (average 43.10 years, SD


31.32-54.88). This reflects the national increase in younger people completing postgraduate education (Norton & Cherastidtham 2018).

The older average age of health information managers (HIMs) (average 48.72, SD 36.56-60.88) and clinical coders (average 46.61, SD 36.29-56.94 is consistent with the findings from previous studies (Australian Institute of Health and Welfare 2010). When these populations were examined, accounting for differences in education levels, those who held a vocational qualification (coder average 50.50, SD 38.34-62.66; HIM average 51.42, SD 40.42-62.42) were approximately 7 years older than those who held a bachelor (coder average 43.08, SD 32.07-54.10; HIM average 44.05, SD 34.08-54.02) or postgraduate degree (coder average 37.48, SD 27.20-54.75; HIM average 43.75, SD 32.19-55.32). This supports the above finding about younger people seeking postgraduate qualifications.

4. Conclusion The HIW is an aging workforce. This will impact productivity, as older workers are working less hours, and may result in a skill and labor shortage within the industry as we do not have the available workforce to replace them when they retire. There is a trend towards younger workers pursuing higher qualifications at a younger age, which needs further exploration.

5. References Australian Bureau of Statistics 2017, Australians pursuing higher education in record numbers, Commonwealth of Australia, Canberra, viewed 28 May 2019 < https://www.abs.gov.au/AUSSTATS/[email protected]/mediareleasesbyReleaseDate/1533FE5A8541D66CCA2581BF00362D1D?OpenDocument >.

Australian Institute of Health and Welfare 2010, The coding workforce shortfall, AIHW, Canberra, viewed 28 May 2019, <https://www.aihw.gov.au/getmedia/68650421-97aa-46cd-811d-20c22ecd061b/11875.pdf.aspx?inline=true>.

Brooke, L 2003, ‘Human resource costs and benefits of maintaining a mature age workforce’, International Journal of Manpower, vol. 24, no. 3, pp. 260-283.

Butler-Henderson, K 2017, Health information management 2025. What is required to create a sustainable profession in the face of digital transformation?, University of Tasmania, Launceston, Tas.

Butler-Henderson, K and Gray, K 2018, Australia’s health information workforce: census summary report 2018, University of Tasmania.

Commonwealth of Australia, Federal Treasurer, 2015 Intergenerational report, CanPrint Communications Pty Ltd., Canberra, ACT, viewed 28 May 2019, < https://static.treasury.gov.au/uploads/sites/1/2017/06/2015_IGR.pdf >.

Health Information Management Association Australia 2016, Health Information Workforce Summit Report 2015, Health Information Management Association Australia, Sydney, NSW.

Health Workforce Australia 2013, Health Information Workforce Report October 2013, Health Workforce Australia, Adelaide, SA.


Legg, M, Lovelock, B 2009, A review of the Australian health informatics workforce, Health Informatics Society of Australia, Melbourne, VIC.

Norton A, Cherastidtham I 2018, Mapping Australian higher education 2018, Grattan Institute, Melbourne, VIC.


Clinical Documentation Improvement Programs: Measuring Performance Nicole Draper1 1CDIA, Sydney, Australia

Introduction Measuring the impact of any initiative is important. Determining the best way to measure the impact can be challenging. When measuring the impact of a Clinical Documentation Improvement (CDI) program there are several key areas that can be measured. Determining what and how you will track your performance is based on the stakeholders and is essential prior to commencing your program.

CDI Performance Monitoring There are several stakeholders in a CDI program, including:

• Hospital executive: CEO, CFO and GM

• Health information team: HIMs and clinical coders

• Clinicians: doctors, nurses and allied health

• Quality managers

• Patients

The CFO and hospital executive will be interested in the overall financial impact of the program, number of DRG changes, number of records reviewed and the records that were unable to be reviewed and why. They may also be interested in the potential impact of a larger CDI presence.

The CDI Program Manager sets the KPI’s for their CDS team. Generally, the baseline KPI’s include, number of patients reviewed, number of reviews, number of queries generated and answered, number of DRG changes and revenue impact. As the CDI program matures there are further KPI’s that are included that go beyond just measuring financial impact. These might include education sessions delivered and attendees. Public and Private Hospitals may choose to report this data based on stream or specialties as well as the overall impact. Health Information Managers are more likely to be interested in the impact the program is having on query rates and coding KPI’s. They will consider if the presence of a CDS is making their coding more efficient.

Clinicians, especially doctors, are often very competitive and will be interested in data and how they compare to their peers.

Quality Managers are constantly reviewing records and wanting a more comprehensive and accurate record of the patient’s episode of care. If undertaking a root cause analysis following an adverse event often information about the care provided is missing.


Patients are at the centre in all that we do in healthcare. Improving clinical documentation impacts on safety, quality and communication. Patients are well informed and want to be part of their care, involving them through CDI can impact on the patients experience and overall outcomes including minimising risk whilst in hospital.

Baseline data will be required, pre- and post- implementation of your program. Data that is included may be:

• DRG split data

• CMI

• Queries generated per week/month by the clinical coders

• Number of discharge summaries

• Coding KPI’s

These areas can be measured at quarterly intervals and feedback to the relevant stakeholders.

Lessons Learnt Regular meetings between the CDS and coding teams will ensure a collaborative working relationship, without this regular interaction the relationship between the two groups can be impacted.

The data being collected through the KPI’s outlined need to be tailored to the audience, providing specialty specific data has the most impact.


Famine to feast – building, developing and retaining a Clinical Coding workforce Grant Duffill1, Meleah Herbert1 1West Moreton Health, Ipswich, Australia

Introduction We are aware that our professions, Health Information Managers (HIMs) and Clinical Coders (CC) are in demand and we continue to face workforce shortages. The purpose of this paper is to outline how West Moreton Health (WMH) has gone from a CC workforce famine to a feast between 2015 – 2019.

After years of a trainee dominated workforce, 2019 has seen the experience level of our workforce shift to predominantly being a senior and advanced level coders. To achieve this shift in skill mix we underwent a restructure, implementation of a digital record, recognition of the skills and abilities of our existing workforce and the ability to offer telecommuting (work from home).

West Moreton Health WMH services a population of approximately 252,000 people which is forecast to increase to an expected 593,000 people by 2036 (an increase of 136 per cent). This projected percentage increase is the largest of any Hospital and Health Service (HHS) in Queensland. The HHS demographics are diverse and include metropolitan and small rural community settings located 30 – 40 minutes west of Brisbane CBD. WMH features Ipswich, Boonah, Esk, Gatton, Laidley Hospitals, The Park Centre for Mental Health and Health Services to five prisons and a youth detention centre.

The Health Information Management Service, which incorporates our Clinical Coding Service, is centrally located at Ipswich Hospital and the team commutes to our rural facilities to undertake HIM and clinical coding activities.

Restructure In 2016, the departure of both the Clinical Coding Manager and Clinical Coding Auditor/Educator within a matter of days provided the opportunity to review the existing structure and submit a new structure to the Executive.

The challenges we faced in WMH was:

• Predominantly paper-based records;

• Neighbouring Health Services (excluding Toowoomba) were all operating a digital record;

• Queensland Metro HHS were paying their CC workforce one pay scale higher;

• Limited positive factors that attract a workforce (e.g. WMH is not the Sunshine or Gold Coast)

Taking into account our challenges, the one that could be overcome immediately was reviewing the pay scales and building a structure that provided employees more opportunities in skills and task complexity.


An improved structure was implemented in the 2017/18 financial year, which was successful, however we still failed to attract experienced staff, continued to have vacancies that were unable to be filled and continued to have a trainee dominated workforce.

Digital Health In November 2018, WMH successfully implemented Queensland Health’s digital solution. The implementation of the digital solution meant we could now recruit Australia wide.

The HIMS Leadership team engaged a third year QUT Student in 2018 as part of their university assessment, to develop telecommuting guidelines and agreements to allow our workforce to work from home.

Telecommuting During implementation of the WMH digital solution, our HIM Leadership received a request from one of our HIM team to telecommute as they were required to relocate outside WMH. This provided our leadership team the opportunity to use this as a test case for our CC.

Since implementation of the digital solution, we have a HIM (3 days per week), an Advanced Clinical Coder (full-time based in NSW) and a second-year trainee (two days per week) telecommuting. A review was undertaken in June 2019, with the recommendation to provide telecommuting to the remainder of our CC and HIM workforce in 2019.

The feast For the first time in 2019, WMH has achieved:

• A fully recruited workforce that is no longer predominantly trainee based;

• A more skilled workforce;

• A structure that allows career progression;

• Greater challenges and satisfaction in their roles;

• Improved productivity contributed directly to telecommuting.

Lessons learnt For twenty years of my HIM career we have been discussing workforce shortages, continual demands, expectations and threats of our HIM and CC professions.

So what lessons have we learnt in WMH?

1. More money did not address our staffing deficiencies;

2. The “Grow your own” model is resource intensive and was detrimental to the culture within the team as they never saw a light at the end of the tunnel due to the continual poaching of our trainees to other facilities;


3. With our digital implementation, we learnt that we needed to focus more on our HIM and CC workforce and provide more clinician consultative education to assist staff with navigating the digital system to ensure quality coding;

4. Communication is paramount when piloting telecommuting as interest within the team is heightened and requests for further expansion of the service exceeded our expectation and disappointed those staff willing to work from home in the fact that they had to wait for the outcome of the pilot.

5. We had to be innovative with how we integrated the full-time telecommute employee into the workplace and make them feel a part of the team.

6. Employees residing in another state are not covered by Workcover Qld and WMH Occupational Health and Safety would not cover these expenses. The HIM budget needed to account for this cost.

7. Recognition of the HIM and CC workforce in West Moreton has grown due to a workforce that is proud of the work they undertake, advocate strongly for both professions and exceed delivery expectations.

The journey since 2015 has been extremely challenging like climbing Mount Everest, waiting for the implementation of a digital solution was like lining up at the summit of the Mountain, and 2019 feels like we have conquered and now on the descent. It has been a long trek that is not over as the continued challenge of retaining staff continues.


It’s all about data! Christine Fan1 1Sydney Children's Hospitals Network, Westmead, Australia

Introduction Health digitisation & the corresponding data explosion is helping to drive a cultural shift where data driven decision making is not only possible, but expected. Our presentation will briefly cover the Sydney Children’s Hospitals Network (SCHN) Business Intelligence journey, demonstrating the power that data can have in improving performance & patient outcomes.

The Business Intelligence journey SCHN has locally developed the Children’s Hospitals Information Management Portal (CHIMP) which serves as the primary location for users to access data. CHIMP contains standard reports, a self-service report function & data visualisations through dashboards.

There are two Health Information Unit specific dashboards, available in CHIMP.

The first pertains to Clinical Coding, which provides transparency on coding performance. The dashboard is updated weekly – automated for Westmead & by data entry for Randwick. It allows performance to be measured & monitored proactivity and early intervention taken, such as increasing the use of casual and contract coders. The dashboard is also referenced by stakeholders, including the Activity Based Funding team, as coded results affect hospital National Weighted Activity Units (NWAU) and performance results.


The second dashboard shows a multidimensional view of key areas of responsibility for medical records including document imaging, medico-legal, transcription, data quality and forms. In addition to providing transparency of service, it allows productivity & turn-around times to be actively managed. It also guides demand for electronic form development, as the most frequently scanned forms can be prioritised for conversion.

Conclusion Dashboards have also been created to monitor quality & performance, and to help manage patient care and flow. Access to transparent, meaningful, accurate & timely information is transforming the health landscape. SCHN applications include:

• The removal of unnecessary interventions for children, ones which are shown to add no value.

• Broadening the options of care by risk stratifying severity, allowing low risk patients to be managed at home.

• Early identification of harm, expediting care management for better outcomes.

The focus on evidenced based decision making has never been so great. SCHN would be delighted to share these improvement opportunities & our interpretation of the new world order.


Development of an Indicator Registry for the Health Roundtable Alix Higgins1 1Potential (x), Surry Hills, Australia

Introduction The use of big data and analytics is crucial to understanding the current healthcare environment. Additionally, it plays an integral role in providing solutions and context to the complexity of healthcare today. To facilitate quality data analytics, a robust and structurally sound information governance process and structure must be implemented (Butler 2017).

Information Governance ensures that information is managed and utilised appropriately through an agreed framework, with a shared understanding of limits, contexts and applicability (Butler 2017).

The Problem The Health Roundtable (HRT) specialises in the use of big data and analytics, to drive change, improve healthcare quality and develop collaboration between organisations. To support this purpose, HRT utilises a number of indicators and measures that provide insights and benchmarking metrics. Recent feedback from members and users revealed that greater visibility, access and understanding of the indicators and measures were desired.

Additionally, as HRT analytics and improvement groups have grown and matured, many indicators have been developed independently of each other. This has led to indicators and measures containing similarly named data concepts however have very different definitions.

To address these concerns, and support future growth for HRT, an opportunity was identified to implement a new system and governance process to manage the indicators and measures produced by HRT.

The Project The indicator project was commenced in 2019, and occurred over four phases, from an initial environmental scan and review, through to development, implementation and then review. The project has an expected completion date in early 2020.

Environmental scan and process review

The initial stage of this project sought to understand the existing governance and organisation structures, and the development processes. During this stage, the preliminary needs and requirements of the new system were also identified. The key outcome from this stage was identifying the preferred solution of an indicator registry based on metadata standards with a structured governance structure.


Stage One: Development and refinement Stage one focused on the development and refinement of the user requirements and identification of product options. This stage ensured that all stakeholders were consulted, capturing the current and future requirements of the organisation. The key outcomes from this stage was confirmation of a software or product solution, development of a Responsible, Accountable, Consulted, Informed (RACI) matrix for the governance process, a comprehensive communication and change management plan for all internal and external stakeholders to support the following stages.

Stage Two: Pilot Stage two introduced piloting and implementation of the new product and governance structure. This approach was designed to capture the issues and process blocks that may not have been identified during the first two phases. The key deliverables from this stage were a functioning framework for indicator and measure review, a governance structure and process, implementation and pilot testing of the software and organisation readiness for full implementation

Stage Three: Implementation

The final stage will encompass the full roll-out and implementation of the new indicator registry, with the governance process and strategy to support it. This stage will also include a project review, to ensure that lessons learnt are captured and able to be fed back to the organisation to support the next major process and program change.

Conclusion The project is still underway, with some key lessons learnt to date. Whilst there is an abundance of literature on the importance of information governance, there is very little literature or case studies similar to this project. This has meant that developing an evidence based approach has been challenging. To compound this, it has been difficult to identify and explore a software/ product solution that meets all the requirements of the project. As a result an innovative and flexible approach that has had to be applied for a complex problem.

References Butler, M 2017, ‘Standards and Information Governance Share a Symbiotic Relationship’, Journal of AHIMA, vol. 88, no. 11, pp. 14–17, viewed 27 May 2019, <http://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=126023795&site=eds-live>.


Why review your internal clinical coding training program Anthea Ho1 1Monash Health, Clayton, Australia

Introduction There is an increasing battle between the supply and demand when recruiting experienced Clinical Coders in Australia leaving many organisations with limited options but to employ staff with little to no work experience.

The organisation is then required to provide training to increase their knowledge and exposure to become work ready coders. While the intention to recruit staff is to increase the productivity within the team, it is apparent that the initial training required is extremely time and resource intensive. Given this, organizations’ should review and evaluate the effectiveness and efficiency of their current training program.

Case Study Description Monash Health is Victoria’s largest public health service that has approximately 18,000 separations per month that includes a broad specialty Casemix. The Clinical Coding workforce comprises of approximately 50 staff with a mix of full-time, part-time, casual and contractors. Predominately, the workforce consists of long term employees (>10 years of service) and young female employees. Given the demographics, on average, there are two EFT on parental leave each year and 39% who are entitled to long service leave.

Prior to the review, the training program took up to two years to complete and produce work ready coders. Given the intensive resources required, it was recognized that the training program needed to be shortened. A working party was developed with the objective to review current processes and to streamline the training program so it better supported coding staff and delivered on organisational requirements.

Implementation The working party was represented by staff across Monash Health sites and departmental roles to ensure all aspects of daily operations were covered. This resulted in a lengthy consultation process that addressed what changes were and were not negotiable to the existing training program. This was required to ensure the coding quality was maintained and that the department was making best use of available coding resources.

Through the review of the training program, issues that were identified with the current process needed addressing. It was indicated that changes were required to shorten the timeframe it takes to have a fully productive coder across all specialties and that a consistent approach needed to be developed across all sites. Support roles were redefined where the responsibilities and the communication matrix was clearly outlined.


In order to shorten the timeframe, the current training modules needed to be replaced as they were too resource intensive and did not allow the trainee to contribute to productive coding. This gave the working party the opportunity to restructure the program and change its processes where the trainee is able to contribute productively at an earlier stage. Specialty training notes were customised where minimal updating would be required which resulted in the release of further coding resources.

A staged approach with a tailored pilot program was deployed to implement the new processes and structure.

Conclusion Through the pilot program, the newly structured training program was trialed and with the feedback provided, it was deemed an overall success that not only reduced the training program from two years to one, but provided a positive experience to all members within the training team.

With regular check-in points developed through the program, feedback indicated that the training program took a more structured approach that provided a clear support system for all members. Trainees felt they were able to contribute to the live coding at an earlier stage and identified it as a personal achievement as they were productively contributing within the team which left them feeling part of a functioning team.

Following the conclusion of the pilot program and the constructive feedback provided, the working party was able to fine-tune and standardise the training program in preparation for its successful rollout across all Monash Health sites.


Australian Emergency Care Classification (AECC) version 1.0 James Katte1 1Independent Hospital Pricing Authority, Sydney, Australia

Introduction Emergency care is the provision of triage, assessment, care and/or treatment. Activity data collected from emergency care facilities comprise a mix of administrative variables (e.g. episode end status, visit type) as well as clinical variables (e.g. triage category, principal diagnosis).

In 2013, IHPA commissioned an investigative review to provide options for improving or replacing the current emergency care classifications. Findings of the review showed these classifications were not suitable for Australia in the medium-to-long term due to the prominence of triage in the classification and limited use of information about patient factors such as the patient’s diagnosis.

Professional practice/case study description The AECC was developed using a combination of statistical analysis and clinical input. The statistical analysis used activity and cost data from emergency departments across Australia. The AECC has a three-level structure:

1. Level 1 splits episodes into those where visit type/ episode end status values are used as the main classifying variables, and those where diagnosis is used. Other emergency care episodes proceed to the next level, categorised by diagnosis.

2. At Level 2, episodes are clustered into clinical conditions managed in emergency care settings. The groups of diagnoses are called Emergency Care Diagnosis Groups (ECDGs). They are based on the Emergency Department ICD-10-AM Principal Diagnosis Short List codes.

3. At Level 3, ECDGs are partitioned into end classes of different levels of complexity, reflecting cost. Complexity splits are based on a score assigned to each episode, which is calculated using the patient’s age group, episode end status, triage category, and transport mode.

The AECC has 181 end classes, which includes end classes not classified by diagnosis (pre-ECDG) and end classes used for episodes with missing or invalid data (error classes).

The AECC (r² = 0.41) outperforms the current classification system (r² = 0.33) when accounting for error classes and outlier episodes.

Implementation/experiences The development of the AECC has been undertaken in several stages including an initial review, a national costing project, regular reviews by the Emergency Care Advisory Working Group and other relevant committees, a public consultation process and a national workshop where attendees were presented with an updated draft of the AECC.


In addition, an independent quality assurance process was undertaken to examine the data and processes followed when developing the AECC and complexity modelling. This process verified the processes described in the final report and were successfully able to replicate the model.

Conclusion – lessons learnt The development of the AECC has been done through significant consultation with key stakeholders including the input of clinical and statistical expertise. The classification has been developed to incorporate more variables as they become available in the national minimum data sets, which was identified as a requirement for the new ABF classification. The quality assurance process identified areas of improvement and enabled an objective review of decisions made in the modelling process.

The AECC version 1.0 has now been finalised and reviewed by IHPA’s advisory committees and the IHPA Board, the Pricing Authority. It is intended that the AECC version 1.0 be used for pricing in 2020-21 with pricing approaches currently being considered.

References https://www.ihpa.gov.au/what-we-do/development-new-emergency-care-classification


Snake Pit to Team of Choice Trixie Kemp1 1Tasmanian Health Service, BURNIE, Australia

Introduction Poor workplace culture can have harmful effects to the individuals in the team and to the team itself through lost productivity, absenteeism, poor morale and time spent on managing these impacts (Heads Up, 2019).

Workplace Bullying in Australia Report (2014) states their research identified stressful work environments, poor communication, weak leadership, and a lack clear policies about workplace bullies as the organisational factors behind workplace bullying. In Australia workplace bullying costs organisations $6 - $36 billion a year (Centre for Health Initiative, 2014).

This paper outlines my approach to changing a poor workplace culture when I started a new role in Tasmania.

Identifying the problems I knew from my interview that I was coming into a team with a poor workplace culture. There were multiple issues and as a result people didn’t want to work in the medical records department.

From my first interactions with the staff I outlined my expectations– accountability, open communication, courtesy and respect. This message was frequently reinforced. I wanted to reinforce these messages to address the team culture.

It wasn’t until I was interacting with other Managers that I learned the team were referred to locally as the ‘Snake Pit’. The poor culture was well known, and I made it my mission to change this.

I met with Human Resources within my first week, who briefed me on the long-term bullying issues in the department and past attempts to address this issue. I also observed the team and noticed:

• Multiple bullies in the team;

• Segregation - groups within groups;

• Dishonesty – staff would tell me one thing, but I would overhear them telling a different story to others;

• Victimisation - staff were scarred to speak up for fear of repercussion or those who did were treated poorly by the bullies;

• Impacts of mood – the mood of the bullies impacted the others. If their mood was good, the team seemed happy, but if they were in a ‘bad mood’, everyone was tense;

• Staff didn’t want to be the team, but didn’t know how to move on;

• Environmental impacts – the office design exacerbated the culture as it created barriers to workflow.


Implementing FISH I decided we needed to become a FISH workplace. I knew about the FISH Philosophy from a leadership course I had completed several years before.

The FISH Philosophy empowers employees to be more effective in any job. When a team lives the philosophy, the workplace culture improves, which results in all round improvements. FISH is used by various organisations globally. It consists of four principles: Be there; Play; Make their day; and Choose your attitude. (FISH Philosophy, 2019)

Applying each of these concepts, as a team we developed different activities, as shown in Table 1.

Table 1. Activities aligned with the FISH Philosophy. Be There Creating a more welcoming space for people. As a team we looked at the

space we had and how to make the most of this space to interact with others and visitors.

One of the easiest activities was moving the reception desk so there was somewhere to welcome visitors to the department.

Play Introduction of casual clothes Friday, with a gold coin donation. The money raised was provided to different charities that the team nominated.

Standard work hours allowed all the team members to interact more, such as chatting while setting up in the morning, taking breaks together, and leaving together.

Make Their Day Each staff member identified something they could do for someone else within or external to the team. These were simple activities and usually viewed as common courtesy, but it was about moving away from looking after only yourself.

Choose Your Attitude

Above the line / below the line team workshop, developing a poster that showed the behaviours and attitudes to be adopted by the team. This established a new set of rules for the team as what behaviours were acceptable and not acceptable. Staff would then be held to account if they did not behaviour in this manner.

Celebrating achievements of the team and individuals, which created positivity

Outcomes There was no one size fits all solution to poor culture. Individual approaches were required for each issue.

Five years on and the people identified through the process as the bullies have left. Other staff who wanted to leave were assisted to find new employment. Sick leave has significantly decreased and staff retention is improved. People now want to work in the department and we have become a team of choice.


Some of the initial FISH activities have gone and new ones have developed, like a daily team stretch session. I have learnt that teams want clear expectations, guidelines and structure; but they also want positivity, fun and support in the workplace.

From my experience organisational culture change takes time, perseverance, resilience, a leader committed to a vision and a team that wants to change. But it is achievable.

References 1. Heads Up, 2019, Workplace Bullying, viewed on 4 July 2019

https://www.headsup.org.au/healthy-workplaces/workplace-bullying

2. Centre for Health Initiatives, 2014, Workplace Bullying in Australia Report, University of Wollongong, viewed on 6 July 2019

https://www.headsup.org.au/docs/default-source/resources/workplace-bullying-in-australia-final-report.pdf?sfvrsn=2

3. FISH Philosophy, Chart House Learning, 2019, Eden Prairie USA, viewed on 24 May 2019, https://www.fishphilosophy.com/


We’ve had a data breach – now what? Kirstie Mountain1 1Department Of Health, Tasmania, HOBART, Australia

Introduction The Department of Health (DoH) Tasmania collects and holds a significant amount of personal information and is therefore at increased risk of breaches of that information.

There is a legal obligation to protect personal information according to the Personal Information Protection Act 2004 (PIP Act) There is currently no requirement in the state’s privacy legislation to report and manage breaches [1].

Data Breach Management Plan Why are we doing this? Despite the fact there is no legal requirement to report and manage breaches in our state’s privacy legislation, we have implemented an internal data breach management plan. This enables us to address information risks as they are identified whilst building capacity from the assessment, reporting, review and prevention of data breaches. It fulfils community expectations that our Agency will act responsibly when it comes to the management of personal information. As yet there is no Tasmanian whole of government data breach policy and procedure, so this plan fills a known gap. Finally, the Agency agrees that ethically and morally it is the right thing to do.

What is a data breach? As the PIP Act does not address data breaches, our data breach response plan follows the principles of the Notifiable Data Breach Scheme run by the Office of the Australian Information Commissioner (OAIC). Where the PIP Act is silent, we draw from the resources of the OAIC.

A data breach occurs when personal information held by an organization is lost or subjected to unauthorized access or disclosure [2].

Not all data breaches need to be reported internally or to the affected individuals, but all data breaches should be documented. Those that are to be reported are those that are likely to result in serious harm to one or more individuals and the Department has not been able to prevent the likely risk of serious harm through remedial action [3].

What is serious harm? This is not defined by the OAIC but can be taken to mean that the risk of serious harm is more probable than not, rather than possible. In the context of a data breach, this may include harm of a serious physical, psychological, emotional, financial or reputational nature. Each case needs to be assessed on a case by case basis [4].

We have used the threshold of likelihood of serious harm to prevent notification fatigue and undue administrative burden.


What is our Data Breach Response Plan? The plan consists of four steps which may occur concurrently. The first step is to contain the breach to prevent any further compromise of the data. The second step is to assess the breach by gathering the facts, evaluating the risks and acting to remediate any risk of harm to individuals affected. The third step involves notifying areas as identified in the governance chart and if advised to do so, notify affected individuals. The fourth step is to review the incident and consider what actions can be undertaken to prevent further breaches. [5]

Resources developed to support Data Breach Management Thus far, the Department has developed the following resources to support the Data Breach Management Plan:

• Position Statement

• Data Breach Notification Form

• Response Flowchart

• Four Step Data Breach Response

• Governance Chart

• Acceptable Use of Information Management Resources

Learnings The plan is not perfect but having something is better than nothing. The plan is a living document that will be updated as we become more experienced as an Agency in managing breaches of personal information. It is not possible to prevent all data breaches, but it is important to identify them and know how to manage them with the aim of reducing the occurrence of preventable data breaches.

It is important to look at data breaches holistically and not just as a cybersecurity issue left in the hands of an information technology security team.

We are learning to define what is serious harm in the context of our Agency and using this as a threshold when it comes to notification to affected individuals.

Implementation of education to staff is required to prevent notification fatigue both internally and externally to affected individuals.

Further work continues to identify who within the Agency needs to be notified of a data breach and under what circumstances. This includes the establishment of a Data Breach Response Team.

Additional resources have not been allocated to the management of data breaches so careful consideration is required to manage data breaches with existing resources.


References 1. Personal Information Protection Act 2004, viewed 6 June 2019

2. Office of the Australian Information Commissioner, February 2018, “Data breach preparation and response – A guide to managing data breaches in accordance with the Privacy Act 1988 (Cth), p8, accessed 6 June 2019, https://www.oaic.gov.au/resources/agencies-and-organisations/guides/data-breach-preparation-and-response.pdf


4. Office of the Australian Information Commissioner, February 2018, “Data breach preparation and response – A guide to managing data breaches in accordance with the Privacy Act 1988 (Cth), pp. 34-35, accessed 6 June 2019, https://www.oaic.gov.au/resources/agencies-and-organisations/guides/data-breach-preparation-and-response.pdf



The Evolving Role of the Clinical Coder in Ireland. Deirdre Murphy1, Maureen Lynn1, Jacqueline Curley1, Marie Glynn1 1Healthcare Pricing Office, HSE, Dublin, Ireland

A study carried recently in Ireland looked at the role of the clinical coder and the structure of coding departments throughout the Irish public system. In particular it considered the impact of ABF and increased focus on activity data and how this is affecting the clinical coders’ role.

Clinical Coders in Ireland work within the Hospital InPatient Enquiry (HIPE) system. HIPE is the principal source of national data on discharges in acute hospitals. HIPE clinical coding teams across all acute public hospitals provide the activity data essential to Activity Based Funding (ABF). The advent of ABF has increased the visibility of HIPE data and clinical coders. Shorter deadlines and increased focus on data quality have added new pressures to the role.

The role of the HIPE clinical coder in Irish hospitals is evolving and although their primary role is still in the clinical coding, their brief has broadened to include; clinician engagement, data reporting, the EHR, documentation improvements, finance and management in hospitals. This is in addition to the on-going responsibilities of HIPE data quality, audit, training and mentoring. With this evolution of the role and the added burden of shorter deadlines (a discharge must be coded within one month of discharge) the demands on the clinical coder are increasingly expanding beyond the HIPE Coding Office. It is more important than ever to have an educated and stable workforce.

The Healthcare Pricing Office (HPO) within the Health Service Executive (HSE) is tasked with supporting the implementation of ABF across the Irish hospital system. The first implementation plan4 made a number of recommendations with regard to increasing the numbers of clinical coding staff nationally. This was in recognition of the central role that HIPE data plays in the implementation and on-going development of ABF.

Previous Irish surveys of the coding workforce have focussed on ‘discharges coded per coder’ without acknowledging the increasingly expanding role of the clinical coders. This study was a timely review of the role of this key hospital staff member; to look at how this job role is evolving; how HIPE Departments are evolving and how best to support the HIPE system.

The Pavilion Report5 (2016) highlighted the lack of career structure for experienced clinical coders. The ABF Implementation plan had also previously made a number of recommendations around staffing. While a number of major hospitals have made significant progress with the structure of

4 Activity-Based Funding Programme Implementation Plan 2015 – 2017, Health Service Executive, May 2015, https://health.gov.ie/wp-content/uploads/2015/07/ABF_Implementation_Plan_20_05_2015.pdf 5 http://hpo.ie/latest_hipe_nprs_reports/Pavilion_Report_National_Audit__Admitted_Patient_Info_Sept_2016.pdf

https://health.gov.ie/wp-content/uploads/2015/07/ABF_Implementation_Plan_20_05_2015.pdf

http://hpo.ie/latest_hipe_nprs_reports/Pavilion_Report_National_Audit__Admitted_Patient_Info_Sept_2016.pdf


their HIPE Coding teams, the project team found the HIPE department structures and line management in some hospitals to be flat and under resourced.

It is important for coder retention that HIPE staff have opportunities to apply for more senior positions within HIPE. In addition potential promotion is an important issue in the retention of these specialised staff. With a clearer career path visible within HIPE, it makes joining and remaining in HIPE more attractive to potential candidates.

During the course of this project it has become clear that the role of the clinical coder is evolving. The clinical coder is expected to take responsibility for their data, for its quality and timeliness. The clinical coder needs to be able to progress within their role for both personal and professional advancement. These specialised staff need to be educated, encouraged and supported to develop in order to support the HIPE system. The HIPE department can be seen as an attractive place for people to work with good opportunities for development, learning and career progression. In turn retention of staff will be supported by a clear career path within the department.

Clinical coders are in short supply both in Ireland and internationally and it is critical that skilled clinical coders are not lost from the system due to shortfalls in career structures, recognition and support.

With clearer job specifications for the different HIPE roles and with a clear management structure within those HIPE departments the evolving role of the coder can be seen as an opportunity for the individual coders, for the hospitals and for the HIPE system nationally. This project will report on results, issues challenges and recommendations from the study.


Natural Language Processing 101 – Understanding NLP and Applicable Uses in Healthcare Laura Pietromica1 1Hyland, Westlake, United States

Introduction The artificial intelligence revolution, and the insurgence of one of its peer applications in healthcare data science, natural language processing (NLP), is upon us. NLP has interesting applications in healthcare yet few people understand it at a basic level. Nor do they recognise the use at healthcare organisations such as transcription software using speech recognition.

In this thought provoking presentation, we will review the basic concepts of NLP, common use cases, and correlations between NLP and medical records.

NLP in Healthcare What exactly is NLP and why should healthcare organisations take notice of this market segment expected to reach $28.60 billion by 2026[1]? NLP is the process by which humans program computers to recognise, learn and manipulate language. Creation of rule engines break down the language into smaller pieces and establishes relationships between them. The rules make it possible for computers to read text and hear speech and more interestingly, interpret and extract meaning.

There are common barriers and challenges with NLP including the complexity and diversity of human language. Influences in language include country and regional dialects. Despite these challenges, speech recognition software thrives. Recent development of skills within virtual assistants and smart home speakers allow users to ask healthcare related questions and receive accurate, reliable answers.

Common Uses in Healthcare User cases vary; however, there are three core drivers: 1) supporting value-based care and population health management, 2) coding and analysing, and 3) decreasing physician workload and burnout. [2]

A common use case for NLP is information extraction. Similar to a clinical coder learning to search for clinical statements to support diagnosis and procedure codes, NLP engines search content for similar indicators. The NLP engine ingests non-discreet, unstructured content and documentation and parses it into critical data elements for analysis. The engine “learns” to repeatedly look for, and parse, these values. For example, when a patient presents for shoulder surgery the NLP engine analyses their medication list. Within minutes, the NLP engine flags the patient for heart failure and the need to continue their medications while preparing for surgery.

https://hitconsultant.net/tag/value-based-care/

https://hitconsultant.net/tag/value-based-care/

https://hitconsultant.net/category/technology/population-health-management-technology/


Abstracting charts is another use case for NLP. An organisation can programme the NLP engine to analyse and summarise blocks of narrative text found in clinical notes and free-text fields. The NLP engine identifies key concepts and phrases to ensure important patient care information and clinical data is not lost. The NLP engine can help the organisation map the information to structured fields in their clinical information system (CIS) or Electronic Health Record (EHR).

Challenges NLP is not an ‘out of the box’ technology. It requires multiple review processes and testing cycles to provide the results desired. Collection of input from end-users requires updating the rules engines. This allows for improvement over time. A second challenge is building the NLP engine to parse compelling information that provides the most value to the most users. The challenge is not just intra-organisation; it is sometimes intra-departmental.

Learning Points The audience will take away the following learning points from this session:

• Basic understanding of NLP;

• Common uses of NLP in the healthcare field;

• Challenges with using the technology; and

• Forward thinking NLP applications.

Conclusion There are big dreams for NLP in the healthcare industry. While there are challenges with NLP, it has many valuable benefits and practical uses. These include making sense of unstructured content and mapping it to CIS or EHR fields. NLP can also help raise awareness for public health initiatives and diseases while also helping organisations determine clinical trial participation matches.

References 1. Market Watch 2019, Natural Language Processing Market (NLP) 2018-2026 | Size, Share and

Forecast | Credence Research, MarketWatch, 5 March 2019, https://www.marketwatch.com/press-release/natural-language-processing-market-nlp-2018-2026-size-share-and-forecast-credence-research-2019-03-05

2. Erica Garvin, 2019, 8 Use Cases for Natural Language Processing (NLP) Technology in Healthcare, HIT Consultant, 22 January 2019, https://hitconsultant.net/2019/01/22/natural-language-processing-nlp-technology-use-cases/#.XO7qERZKiUk


Clinical documentation specialist (CDS): the role, its implementation and path to success. Joanne Pitsaris1 1Ramsay Health Care, Melbourne, Australia

Introduction While flourishing in the United States for over a decade, Clinical Documentation Improvement (CDI) has in recent years also arrived at the shores of the Australian hospital landscape. With benefits in improved quality, patient care, resource utilisation, safety outcomes and reimbursement, public and private sector hospitals have introduced roles such as the Clinical Documentation Specialist (CDS) in a quest to tackle CDI. While the case for CDI is quite clear, however, the creation and implementation of the CDS role can be a minefield. In this presentation, practical strategies on how to implement and cement the role of the CDS in an organisation seeking CDI and its positive outcomes will be discussed.

The Role The aim of the CDS role is to ensure documentation is clear, concise and above all, that it accurately reflects a patient’s story. The ability to undertake documentation reviews while the patient is in hospital also helps the CDS to bridge any gap between clinicians and coding professionals; an outcome that leads to a reduction in post-discharge queries from coders. The CDS role can also act as a conduit for the coding teams to raise issues that influence the implementation of CDI strategies by hospitals.

Implementation An example of a successful implementation of the CDS role is seen in Ramsay Health Care. In June 2017, CDSs were appointed from nursing backgrounds across 40 sites in Australia. Each CDS was given five days of 3M training with their DRG assurance program. This training provided an understanding of ICD disease codes, groupers, DRG’s, complications and comorbidities and how they influence DRG mobility; it did not, however, seek to make CDSs coders. Furthermore, training was also provided on how to formulate ethical queries to clinicians in a concise manner.

When implementing a CDS in any hospital, success very much relies on executive support. The introduction of the CDS should be communicated to all doctors, nurses and allied health care staff prior to commencement. This sets the expectation that documentation queries will be received and it gives the role formality and legitimacy. The CDS should also aim to implement a query system for clinicians that is easy to follow and which also makes it clear to coding staff that a CDS has reviewed a particular file. Indeed, the success of the CDS lies in the fact that beside every successful CDS is a phenomenal clinical coding team.


Skills needed Positive, dynamic and great interpersonal skills must be part of the selection criteria when choosing a CDS. Communication skills and diplomacy are also essential to the role, as any quest for clarification must not come across as challenging medical knowledge and expertise. The ability to deal with conflict and ingrained behaviours is also part of the territory. Finally, the relationship between a CDS and the coding team needs to be collegial and respectful. A CDS possessing these types of skills will go a long way to shifting misconceptions about CDI.

Conclusion The CDS role is at the pioneering stage of CDI in Australia. Working individually or as part of a team, the CDS can provide a complete story of a patient’s hospital journey. Through increased engagement and the development of close-knit relationships with clinicians and the coding team, the CDS can truly bridge the gap between these two worlds and ultimately improve outcomes for a hospital and its patients.


Establishment and successful implementation of a coding from home program Kara Pollard1 1Central Coast Local Health District, Gosford, Australia

Introduction Clinical Coders form an integral part of the health service, converting patient’s health record information into alphanumeric codes according to the International Classification of Disease (ICD-10). These codes form part of a data collection that is used for funding, research and health care planning.

Clinical Coders need to have high level knowledge of human anatomy, surgical procedures, the process of disease and attention to detail to produce correctly coded episodes. Therefore, the attraction and retention of senior and experienced coders is vital to the Health Services.

To assist in successful recruitment and retention, NSW Health Central Coast Local Health District (CCLHD) implemented a coding from home program in December 2016. This program allows Clinical Coders to work from home, with a requirement to present to the office a minimum of one day per week.

Method Implementation of the program included:

1. Establishing a working from home agreement for clinical coding staff.

o The agreement document prepared for the coding staff is fluid. Providing for regular reviews in line with CCLHD Core Values and performance. Measures. The fluidity of the document allows amendments of the conditions in times when it is deemed that more ‘office’ presence is required, i.e. coding edition changes.

o The agreement document was also benchmarked across other working from home sites both nationally and internationally.

2. Establishing a workplace health and safety assessment process in conjunction with Workforce and NSW Health policies.

o This involved home office set up and management. Staff are required to complete a WH&S assessment on their work area at home, and submit photo evidence of this.

3. Accessing ‘remote access’ technology for staff to use the eMR from home computers.

o A unique part of the CCLHD agreement, is that the coder themselves are responsible for providing all equipment to work from home. This ensures minimum set up costs for the Local Health District, and allows the staff autonomy in their technology set up.

4. Introducing innovative communication initiatives such as a Clinical Coding Team Site, Skype and teleconference meetings.


o A yearly program is provided to the coding staff that outlines the commitment we expect in terms of attendance on teleconference meetings, manager catch ups, and weekly education huddles. New technologies for off-site conferencing are explored and implemented when shown successful (e.g Skype, Zoom).

5. The development of a sound education program which allows for regular in- office education day,

o This has continued a team atmosphere by fostering a peer mentoring experience.

6. Introducing staff reviews to ensure coding quality and throughput is achieved in the home environment.

o Staff are regularly audited for their coding accuracy and throughput along with performance reviews to ascertain their satisfaction levels and future goals and needs.

Review Lessons learned/ where to from here:

• While this has been a work in progress for a couple of years now, there is continual evaluation and adjustment being made.

• The future expansion of the coding from home model will greatly depend on the outcomes of audits and education by the CCLHD Coding Management team.

• Previously, due to lack of management resources, some ‘bad habits’ crept in which highlighted the necessity of ongoing evaluation, review and implementation.

• It is expected that clinical coding will become more efficient with greater monitoring and analysis by coding management.

• It would be a recommendation to facilities looking to start this process to set realistic guidelines and expectations which are managed effectively. Regular review and reporting is vital to the success of the program.

Conclusion CCLHD has successfully implemented a coding from home program that is now used by 90% of Clinical Coders. Currently the terms of this success are measured at regular performance reviews of staff, coding quality and the commitment of CCLHD clinical coders to engage with management and their peers when required. This program has been directly responsible for the recruitment of 2 senior coders, 2 entry level coders and the retention of 2 staff on a casual basis. It continues to be a major factor when interviewing for new staff, as it is a very attractive consideration for applicants.

References AIHW- 23 Nov 2010, The Coding Workforce Shortfall, Available at: https://www.aihw.gov.au/reports/workforce/the-coding-workforce-shortfall/contents/summary [Accessed 27/07/2018]

https://www.aihw.gov.au/reports/workforce/the-coding-workforce-shortfall/contents/summary


Factors associated with coding quality: comparison of Stroke Registry and administrative data Olivia Ryan1, Merilyn Riley3, Sibilah Breen1, Kate Paice1, Sam Shehata1, Natasha Lannin4,5, Dominique Cadilhac1,2, Monique Kilkenny1,2

1Stroke Division, Florey Institute of Neuroscience & Mental Health, Heidelberg, Australia, 2Stroke and Ageing Research, School of Clinical Sciences at Monash Health, Monash University , Melbourne, Australia, 3College of Science, Health & Engineering, La Trobe University, Bundoora, Australia, 4Faculty of Health Sciences, La Trobe University, Bundoora, Melbourne, Australia, 5Occupational Therapy Department, Alfred Health, Melbourne, Australia

Introduction The International Classification of Diseases and Related Health problems, 10th edition (ICD-10) is used for a range of administrative, economic and epidemiological purposes. Specifically, ICD-10 coded data provides an estimate of nationwide public and private hospital separations, assists in the identification of patient cohorts and provides follow-up information for public health and comparative effectiveness research (Cadilhac, Vos, & Thrift, 2013; Hall et al. 2016). The translation of clinical stroke diagnoses into ICD-10 coded data is an essential component of healthcare systems for systematically monitoring stroke cases around the world. However, the potential misclassification of stroke and its pathological types in ICD-10 coded administrative data is largely unknown. The aim of this study was to a) investigate the concordance (i.e. quality) of ICD-10 stroke diagnosis codes recorded in hospital administrative data compared to clinical quality registry data, and b) determine whether patient characteristic (e.g. age, sex), health system (e.g. hospital teaching status) or clinical process of care factors (e.g. treatment in a stroke unit) are associated with the quality of ICD-10 coded stroke diagnoses.

Methods Data from the Australian Stroke Clinical Registry (AuSCR) provided by 39 hospitals from 2009-2013 with person-level linkages to government-held administrative data across four Australian jurisdictions (Victoria, New South Wales, Queensland and Western Australia) enabled validation of ICD-10 coded stroke diagnoses. The clinician-assigned stroke diagnosis was used as the reference standard for the stroke diagnosis. Concordance was defined as agreement between the AuSCR clinical-assigned stroke diagnosis and principal ICD-10 diagnosis codes commonly used for stroke (ICD-10 codes: I61-I64, G45.9). Positive predictive value (PPV), sensitivity and the proportion of false negative and false positive records and the alternate codes for discordance were calculated by stroke type.

Proportions and chi-square tests were calculated for categorical factor-related variables for concordant and discordant records. Multiple logistic regression was performed to investigate the factors associated with differences in the quality of stroke ICD-10 coding. Odds ratios (OR) with 95% confidence intervals (CI) were calculated and a p-value of <0.05 was considered significant.


Results A total of 14,716 stroke admissions were evaluated (46% female, 63% ischaemic stroke (IS), 14% intracerebral haemorrhage (ICH), 18% transient ischaemic attack (TIA) and 5% undetermined based on AuSCR clinical stroke diagnosis [reference standard]). The concordance with ICD-10 codes was greatest for ICH and TIA compared to IS and undetermined stroke (77% and 80% versus 72% and 51% respectively). The main reason for discordance was misclassification of unspecified stroke (ICD-10 code: I64) for records with a more specific diagnosis recorded in the AuSCR. Concordance for ischaemic stroke coding was greatest for patients admitted to metropolitan (OR 1.65, [95% CI 1.43,1.90], p<0.001) and teaching hospitals (OR 1.44, [95% CI 1.29,1.61], p<0.001), as well as for patients treated in a stroke unit (OR 1.52, [95% CI 1.33,1.73], p<0.001). Hospitals with <200 beds were less likely to record a concordant ICH coded diagnosis compared to larger-sized hospitals (i.e. >500 beds) (OR 0.82, [95% CI 0.69,0.97], p<0.001). Concordance for ischaemic stroke coding was also greatest for patients discharged to inpatient rehabilitation (OR 1.22, [95% CI 1.04,1.42], p<0.001).

Conclusion The quality of Australian ICD-10 coded administrative data varied significantly according to stroke type. Substantial coding discrepancies between ischaemic stroke and undetermined stroke; and TIA and ischaemic stroke compared to the clinical quality registry reference standard data were largely responsible for suboptimal concordance. Clinicians, policy analysts, health system administrators and researchers should remain aware of the limitations of using administrative data for valid identification of patients with stroke. Further, the ICD-10 coded stroke diagnosis data also varied according to a range of clinical process of care and health system factors, such as hospital teaching status. Targeting of these factors should be prioritised in future stroke coding improvement research and the development of a national stroke coding education program is recommended to enhance the utility of coded administrative data for stroke research and management.

References Cadilhac, DA, Vos, T, & Thrift, AG 2013, ‘Estimating the annual number of strokes and the issue of imperfect data: an example from Australia’, International Journal of Stroke, vol. 9, no.1, pp.19-22.

Hall, R, Mondor, L, Porter, J, Fang, J, & Kapral, MK 2016, ‘Accuracy of Administrative Data for the Coding of Acute Stroke and TIAs’, The Canadian Journal of Neurological Sciences, vol.43, no.6, pp.765-773.


Capturing the Experiences of Survivors of Torture and Trauma Carlena Phi Phuong Tu1,2 1STARTTS, Carramar, Australia, 2IRCT, Copenhagen, Denmark

Introduction According to the United Nations Convention against Torture (United Nations 1984), torture is:

any act by which severe pain or suffering… is intentionally inflicted on a person for such purposes as obtaining… information or a confession… when such pain or suffering is inflicted… with the consent or acquiescence of a public official or other person acting in an official capacity…

A Sri Lankan male survivor (Hughes 2013, p. 80) described torture as:

I wasn’t tortured. Not like the others around me. I was just hung and beaten for a few days. They tortured others in the camp… They would put barbed wired in a [PVC] pipe and insert it up them. Then they would take the pipe out and leave the barbed wire inside.

In the last 5 years, Amnesty International (2019) has reported torture in 141 countries worldwide. People tend to avoid thinking about this – the fact that torture still has a place in the world and that survivors struggle to have a life after torture (Aroche 2018).

Capturing the Experience The Forum of Australian Services for Survivors of Torture and Trauma (FASSTT) includes members in each state and territory of Australia (FASSTT 2017, p. 4). In 2014, the National Minimum Dataset (NMDS) was introduced painting the demographic profile of torture survivors in Australia. In 2010, psychological symptoms were consistently collected and in 2018, members would collect information on torture and trauma experiences (FASSTT 2017, p. 8). This allowed for evidence-based advocacy, presentation of evidence where perpetrators are brought to justice and a better environment for survivors to heal (FASSTT 2017, p. 24-25).

The International Rehabilitation Council for Torture Victims (IRCT) is the leading membership-based organisation specialised in the field of torture rehabilitation (IRCT n.d. a).

The Anti-Torture Database (ATD) was implemented in February 2015 under the Data in the Fight against Impunity (DFI) project. This project involved 33 centres in 28 countries around the world (IRCT 2017). It identified the strength of survivor stories. These stories painted the effect torture has on victims, families and communities. Executors of torture and those that need to be held responsible are present in these stories (IRCT 2017).

The objective of the IRCT in its 2018-2020 Strategy is to “improve the quality of life of torture victims by guaranteeing access to health-based rehabilitation services”. Clinicians document


significant information. By standardising data collection principles, centres are able to evidence their work, hold States accountable for torture and improve the quality of interventions provided to survivors (IRCT 2018b, p.5). The Global Anti Torture Evidence (GATE) project worked with 15 centres globally by introducing a standardised health information system (ATD) to record information under strict ethical and safety standards (IRCT 2018b, p.6). The information allows centres to pursue evidence-based advocacy driving strategic interventions at a regional, national and international landscape (IRCT 2018b, p.7).

The IRCT Data and Research Methods Reference Group (DRG) was formed in 2018. The members act as subject-matter experts to “advise on the standards applicable to those engaged in IRCT data collection and its use as part of the IRCT Data Programme”. The group consists of fifteen global members – two from Australia. The only Health Information Manager member is from Australia (IRCT n.d. b). The unique experience and knowledge of a Health Information Manager provides expert advice on the following key topics:

• Client confidentiality including consent, collecting, storing, transferring and using clinical data

• Management of robust, secure, reliable and accurate clinical record keeping systems

Although the Anti-Torture Database (ATD) provided a health information system aligning data principles, it would be impractical to implement this system to other torture and trauma centres with existing high-functioning health information systems. A key project of the Data Reference Group is to create a Global Minimum Dataset (L. Haagensen, personal communication, 30 April 2019). This will drive comparable data to a global level and further provide a platform for survivors of torture to share their stories, attain justice and continue their lives after torture.

“We believe that by mobilising clinical data about torture we can empower torture rehabilitation centres to create positive change in the lives of individual victims and in the global fight against torture” (IRCT n.d. b).

References Amnesty International 2019, Torture: A Global Crisis, Amnesty International, London, viewed 24 April 2019, https://www.amnesty.org/en/get-involved/stop-torture/.

Aroche, J 2018, ‘Dangerous times’, Refugee Transitions, October 2018, Issue 33, p. 27-29, viewed 24 April 2019, http://www.startts.org.au/media/startts_refugeetransitions33_web.pdf.

Forum of Australian Services for Survivors of Torture and Trauma 2017, Never Turning Away – Australia’s World-Leading Program of Assistance to Survivors of Torture and Trauma (PASTT), FASSTT, Fairfield Queensland, viewed 3 May 2019, https://fasstt.org.au/wordpress/wp-content/uploads/2018/12/FASSTT_BOOKLET_2017_A4_FA_web.pdf.

Hughes, D 2013, Violence, Torture and Memory in Sri Lanka, Taylor and Francis, London, United Kingdom.

International Rehabilitation Council for Torture Victims n.d. a, About the IRCT, IRCT, Copenhagen, viewed 3 May 2019, https://irct.org/who-we-are/about-the-irct.

International Rehabilitation Council for Torture Victims n.d. b, Global Torture Data, IRCT, Copenhagen, viewed 24 April 2019, https://irct.org/campaigns/global-torture-data.


International Rehabilitation Council for Torture Victims 2017, The Data in the Fight against Impunity (DFI) Project, IRCT, Copenhagen, viewed 3 May 2019, https://irct.org/uploads/media/0aeabac6e3e9c185e345f2876cb82607.pdf.

International Rehabilitation Council for Torture Victims 2018a, IRCT Strategy 2018-2020, IRCT, Copenhagen, viewed 3 May 2019, https://irct.org/assets/uploads/pdf_20180209151713.pdf.

International Rehabilitation Council for Torture Victims 2018b, The Gate Project – Innovate. Empower. Improve, IRCT, Copenhagen, viewed 3 May 2019, https://irct.org/uploads/media/IRCT-TheGATEproject_2_(web).pdf.

United Nations 1984, Convention against Torture and Other Cruel, Inhuman or Degrading Treatment or Punishment, United Nations Treaty Collection, New York, viewed 24 April 2019, https://treaties.un.org/doc/Treaties/1987/06/19870626%2002-38%20AM/Ch_IV_9p.pdf.


Posters


Improving Patient Alert Data Quality in Mater's Patient Administration System (PAS) Joy Smith1 1Mater Group, South Brisbane, Australia

Mater Group’s patient administration system is iPM, which holds over 1.5 million records for seven facilities and has been in place for over 10 years.

As HIMs know, the PAS – incorporating the Patient Master Index, as it does – is a core component of all our systems, whether we have paper-based health records or a network of electronic information systems. At Mater, of the hundreds of our active systems, iPM is considered to be “the source of truth” for patient-related data and with so many other downstream systems relying upon it, safeguarding it is an important but very demanding task.

In early 2019, after conducting a brainstorming session with representatives from a wide range of stakeholders from across the organisation and compiling the raised issues into themes, several working groups were created, each of which would review a key aspect of the system to identify any data quality issues, quantify them, uncover their causes, devise ways to eliminate or mitigate them, and come up with an implementation plan to put them into action. The six nominated target areas were: Admissions, Discharges and Transfers, Alerts Management, Clinics, Patient Declaration and Consent, Contracts, Patient Registration and GP Details.

This poster describes our journey to improve and sustain the quality of patient alerts data that are maintained in the system for the safety of patients, the benefit of the staff and the organisation. The tool used is A3 Problem Solving, first employed by Toyota and described by Sobek and Smalley (2008) and many others.

References Sobek, Durward K.; Smalley, Art (2008). Understanding A3 thinking: a critical component of Toyota's PDCA management system. A Productivity Press book. Boca Raton, FL: CRC Press/Productivity Press.


Workshops


Making Lean Six Sigma work for you Paraic Bergin1

1Hamad Medical Corporation, Doha, Qatar

Target audience: HIM’s, Clinical Coders, Clinical Documentation Specialists

Learning needs/objectives: The workshop is designed to help participants to

1. Assess a problem or area for improvement.

2. Identify possible routes to dealing with it.

3. Evaluate potential solutions.

4. Choose an approach that has a reasonable chance of success.

5. Select tools and methods suited to the problem and the approach chosen.

6. Use the tools and methods to address the problem.

Background While Lean is now regarded as synonymous with the Toyota Production System (TPS), pure lean is concerned with the elimination of waste (“muda”). Lean methodologies “allow an organisation to efficiently and effectively design workflow processes by eliminating mudas (or wastes)” (Kuo, Borycki, Kushniruk and Lee, 2011, p.5).

The seven original “muda” are transport, inventory, motion, waiting, over-production, over-processing and defects. The TPS has two fundamental pillars, Just-in-Time and Automation, is concerned with the improvement of flow and focuses on three key forms of waste

• “muda” is work which adds no value;

• “muri” is work which is too complicated or uncontrolled; and

• “mura” is work which lacks uniformity or consistency.

Six Sigma, developed and applied in manufacturing industry, is a “process-focused, statistically based approach to business improvement…” (Bisgaard, Hoerl and Snee, 2002, p.701). The structured method used is to Define, Measure, Analyse, Improve and Control (DMAIC) and the core performance metric is the number of defects per million opportunities (DPMO) (Schroeder, Linderman, Liedtke, Choo, A.S., 2008).

Lean Six Sigma is an amalgam of aspects of each, balancing the qualitative, value driven of Lean’s doing the right thing with Six Sigma’s empirical quantitative measurement of doing things right.

The framework introduced in this workshop was developed in an attempt to answer the apparent failure of Lean Six Sigma in healthcare.

While the framework is focused on Lean Six Sigma, it is not necessary to adopt the philosophy as the tools and methods can be, and have been, utilised in many different settings.


Structure The structure of the workshop is to explore and discuss:

(i) The topic of Lean Six Sigma, including strengths, weaknesses and critical success factors;

(ii) What makes healthcare different from other settings;

(iii) The concept of simplification as an alternative to complexity and intensification;

And to then introduce

(iv) A new decision support framework designed for the acute hospital setting; and

(v) The seven basic tools of quality

The final portion of the workshop is use the framework to examine some real life case studies (projects completed in Hamad Medical Corporation or case studies brought by participants).

Learning Strategies Participants will learn from

(a) Exploring and discussing the philosophy and some of the methods and tools of Lean Six Sigma;

(b) Examining the fundamental differences between healthcare and most other businesses, especially key characteristics unique to healthcare, including individual patient variation and clinical independence;

(c) Considering the usefulness and transferability of a decision support framework designed for the acute hospital setting;

(d) Working with real life examples to better understand the framework, methods and tools proposed.

Intended outcomes Participants will be

• Confident of their ability to identify improvement opportunities and appropriate ways to approach and deal with them.

• Aware of the principal reasons Lean Six Sigma has not been successful in healthcare.

• Aware of how to use the framework to evaluate options and select appropriate solutions.

• Confident of their understanding of and ability to use the seven basic tools of quality.


References: Bisgard, S., Hoerl, R.W., Snee, R.D., 2002. Improving Business Processes With Six Sigma. Quality Congress ASQ's Annual Quality Congress Proceedings, pp.701-704

Kuo, A. M-H., Borycki, E., Kushniruk, A., Lee, T.-S., 2011. A Healthcare Lean Six Sigma System for Postanesthesia Care Unit Workflow Improvement. Quality Management in Health Care, 20(1), pp.4-14

Schroeder, R.G., Linderman, K., Liedtke, C., Choo, A.S., 2008. Six Sigma: Definition and underlying theory. Journal of Operations Management, 26(4), pp.536-554


#MeToo in HIM: understanding sexual violence and the ethical bystander Kerryn Butler-Henderson1, Richard Lawrance2

1University of Tasmania, Launceston, Australia, 2Health Information Management Association of Australia, North Ryde, Australia

Introduction The Australian Human Rights Commission (2019) defines sexual harassment as “any unwanted or unwelcome sexual behaviour, which makes a person feel offended, humiliated or intimidated.” Under the Sexual Discrimination Act 1984, sexual harassment is a type of sex discrimination and is unlawful in some circumstances. Yet 1 in 5 women and 1 in 20 men report experiencing sexual harassment in the Australian workplace. Sexual harassment can be committed by an employer, subordinate, client, or workmate. It can lead to internalised sexual repulsion, difficulties forming relationships, and an inability to form connections with peers. Very few people who experience workplace sexual harassment make formal complaints, which may be due to a lack of understanding about what constitutes sexual harassment.

We, as ethical bystanders, have a responsibility to support those who experience sexual harassment. The Sexual Harassment: Know Where the Line Is campaign is a national awareness raising strategy between the Australian Human Rights Commission, the Australian Council of Trade Unions, and the Australian Chamber of Commerce and Industry. The campaign intends to prevent and reduce the harm of sexual harassment in Australian workplaces. This workshop aims to inform participants about the definition of sexual harassment, assist them with identifying their responsibilities as an ethical bystander, and provide them with the tools to create safe workplaces for themselves and their colleagues.

TRIGGER WARNING:

This workshop will include example of sexual violence. The workshop is not facilitated by a clinical psychologist and therefore participants are advised to not attend if they believe exposure to this content may cause psychological or other harm.

Target audience Everyone


Learning outcomes By the conclusion of this workshop, participants will be able to:

1. Define what is sexual violence;

2. Differentiate the different types of sexual violence;

3. Articulate the contributions the Sexual Discrimination Act 1984 and the Sexual Harassment: Know Where the Line Is campaign can make in the reduction of incidence of sexual violence in the workplace;

4. Develop methods of prevention and responding to sexual violence in the workplace.

Structure and learning strategies The workshop will start with defining sexual harassment and sexual assault. Current statistics will be provided to contextualise the issue. Participants will identify and discuss various scenarios.

Next, the Sexual Discrimination Act 1984 will be examined, including the employers’ legal liability. The Sexual Harassment: Know Where the Line Is campaign will be presented and the contribution both Act and campaign can make in the reduction of sexual violence in the workplace discussed

Lastly, the concept of the ethical bystander will be outlined, and participants will identify what they can do if they are a victim of sexual violence or if they witness or are told about someone else experiencing sexual violence. The group will discuss methods of prevention and responding to sexual violence in the workplace.

The workshop will use a mixture of presentations, videos, polling, discussion, and case scenarios.


Benefits Realisation Planning for Digital Health Projects Alicia Cook1

1Emerson Health, Summer Hill, Australia

Target Audience Health Information Managers, those in or aspiring to Leadership Roles, Project Managers

Learning Needs/Objectives The process of moving to digital platforms is a substantial capital investment, but there are challenges in ensuring anticipated benefits identified in a business care are realised.

The purpose of this session is to help participants better understand:

• Why benefits realisation is important, and when to start benefits planning activities

• Consider the stakeholder groups that might benefit from a given digital health project and the impact of varying perspectives on benefits realisation planning

• How health leadership can use benefits realisation planning to support strategic decision making

Structure and learning strategies The session is delivered as an interactive 90-minute workshop based on a fictional digital health case study. The scene is set using a slide presentation, and participants will be broken into groups to complete a series of exercises to underpin basic benefits realisation planning.

Templates and tools will be provided to the group.

The content will include:

• Setting the scene through describing the strategic drivers impacting on the health sector more broadly

• Discussing the paradox of health technology

• Discussing current approaches to digital health investment

• Fictional Case Study

• Exercise 1: Beneficiaries

• Exercise 2: Measuring Benefits

• Exercise 3: Timeframes for Benefits Realisation

• Group Discussion and Reflection

• Invitation to provide feedback


Intended outcomes Participants will

• Engage at a high level with a methodology for benefits realisation planning

• Establish a general understanding of how to apply that methodology

• Develop an improved understanding of benefits measurement

• Apply critical thinking to identification of metrics which may be used for benefits realisation


AR-DRGs: What's New in Version 10.0 Anne Elsworthy1, Sean Heng1, Ning Ding1, Heon Lee1

1Independent Hospital Pricing Authority, Sydney, Australia

Introduction The Independent Hospital Pricing Authority (IHPA) is responsible for the development of the Australian Refined Diagnosis Related Groups (AR-DRGs) classification. AR-DRGs group together treatments and services provided for admitted acute care to enable hospitals to be funded for these services using Activity Based Funding (ABF) arrangements. AR-DRG Version (V) 10.0 is the first version developed in-house by IHPA.

The AR-DRG classification is underpinned by the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM), the Australian Classification of Health Interventions (ACHI) and the Australian Coding Standards (ACS); collectively known as ICD-10-AM/ACHI/ACS

All public and private hospitals in Australia use ICD-10-AM/ACHI/ACS and AR-DRGs to classify admitted acute care and the classifications are updated every two years to ensure they are fit for purpose and remain clinically current.

Following assignment of ICD-10-AM and ACHI codes episodes of care are assigned to a DRG in the AR-DRG classification. AR-DRGs group episodes of care with similar diagnoses and intervention codes.

The AR-DRG classification consists of approximately 800 end classes, with each admitted acute episode of care being classified based on diagnoses, interventions and other routinely collected data, such as age, sex, mode of separation, length of stay, newborn admission weight and hours of mechanical ventilation. While the AR-DRG classification is instrumental to ABF, it is also used for many other purposes including performance management, benchmarking, epidemiology and research.

It is anticipated that AR-DRG V10.0 will be released in mid-2019 and used to price admitted acute episodes of care from 1 July 2020.

This workshop will cover the basic concepts of how the AR-DRG classification works, key refinements undertaken for V10.0 and will provide insight as to how the classification interacts with other classifications and digests clinical information to inform the final AR-DRG end classes

Target Audience The workshop is aimed at health information managers, clinical coders, clinicians, casemix data users and anyone with an interest in AR-DRGs.


Learning Needs and Objectives While many Health Information Managers and Clinical Coders have a basic understanding of AR-DRGs and the way in which this classification interacts with the underpinning ICD-10-AM/ACHI/ACS; in many instances this knowledge is limited.

A major objective of the workshop is therefore to improve the knowledge and insights of participants in terms of how the AR-DRG classification operates and integrates with other classifications.

Structure and learning strategies The workshop will be structured and will have several presenters. It aims to engage participants using live interactive polling software where they can ask questions and vote on different issues.

The workshop also demonstrates V10.0 changes by using themed scenarios built around popular TV shows and movies.

Intended outcomes The expected outcomes of the workshop are that participants gain an understanding of AR-DRGs or build on the knowledge they already have with respect to the:

• Basic constructs of the AR-DRG classification

• Interaction between AR-DRGs and ICD-10-AM/ACHI/ACS

• Major changes for AR-DRG V10.0

Key areas that will be covered in relation to V10.0 include:

• Diagnosis exclusion review

• Review of the intervention hierarchy

• New and deleted ADRGs

• Diagnostics used to inform splitting of DRGs into their end classes.

The workshop will also cover the underpinning constructs of the AR-DRG classification:

• Clinical coding and data collection

• Understanding ICD-10-AM/ACHI and ACS

• The structure and layers of the AR-DRG logic

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Incorporating Research into Your Everyday Role Jeewani Anupama Ginige1, Mary Lam2, Kerryn Butler-Henderson3, Vicki Bennett4, Tanija Tarabay5, Gavin Lackey6, Gowri Sriraman7

1Western Sydney University, Penrith, Australia, 2University of Technology , Ultimo, Australia , 3University of Tasmania, Launceston, Australia, 4Australian Institute of Health and Welfare , Bruce, Australia, 5eHealth Queensland, Fortitude Valley, Australia, 6South Eastern Sydney Local Health District, Darlinghurst, Australia, 7Sydney Children's Hospitals Network, Westmead, Australia

Introduction The results of a recent survey of Health Information Management (HIM) and Clinical Coding (CC) practitioners found a poor, if not non-existent, research culture in practitioner roles, with lack of time, recognition and organisational/role support to incorporate research into everyday HIM and CC roles (Rupnik et al 2017). This finding is supported by the literature on other health professions, which reports that research is often not a priority within the work day, and there is a lack of and support to undertake research (Marshall et al. 2016; Akerjordet, Lode & Severinsson, 2012; Johnson et al. 2014; Rahman et al. 2011). Furthermore, there is a lack of interest, knowledge, and experience among practitioners, in addition to a paucity of resources (Rupnik et al 2017, Akerjordet, Lode & Severinsson, 2012). The recommendation from Rupnik et al (2017) is HIMAA needs to advocate for research to be incorporated into practitioner roles and for increased professional development opportunities to provide practitioners with the knowledge and skills to identify opportunities and incorporate research into everyday roles. The HIMAA Research Advisory Committee (RAC) has included in its workplan initiatives to provide mentorship to members in all matters related to undertaking research. This includes providing a workshop at the annual national conference. The aim of the 2019 workshop is to provide participants with the skills to identify a suitable project in an everyday workplace setting that will have research value.

Target audience Anyone working in a role where the primary function is not research related, who would like to learn more about how to undertake research in their role.

Learning outcomes By the conclusion of this workshop, participants will be able to:

• Identify a suitable project in the workplace that will have potential research value;

• Incorporate research whilst minimizing the time commitment;

• Evaluate resources, support and funding required to undertake research;

• Justify the workplace value of HIM professionals doing research;

• Learn how to build a research culture within a service.


Structure and learning strategies The workshop will start with demystifying common misconceptions about incorporating research into a practitioner’s role and the value of undertaking research, to the individual, the organisation and the profession.

The facilitators will then work with participants to identify a suitable project in the workplace that will have potential research value. This includes both identifying opportunities and turning workplace activities into research projects.

Next, the workshop will show participants how they can manage their time efficiently so time does not become a barrier to incorporating research in everyday roles. Participants will be provided with the skills to identify resources, support and funding within their own organisation. This includes building on their knowledge of conducting research, and developing the justification for why incorporating research into a practitioner’s role should be valued.

Lastly, participants will learn how to build a sustainable research culture within their service and how to collaborate with others to facilitate ongoing research opportunities.

References Akerjordet, KM, Lode, K & Severinsson, E 2012, ‘Clinical nurses’ attitudes towards research, management and organisational resources in a university hospital: part 1’, Journal of Nursing Management, vol. 20, pp. 814–23.

Johnson, C, Lizama, C, Harrison, M, Bayly, E & Bowyer, J 2014, ‘Cancer health professionals need funding, time, research knowledge and skills to be involved in health services research’, Journal of Cancer Education, vol. 29, pp. 389–94.

Marshall, AP, Roberts, S, Baker, MJ, Keijzers, G, Young, J, Stapelberg, NJC &Crilly, J 2016, ‘Survey of research activity among multidisciplinary health professionals’, Australian Health Review, vol. 40, pp. 667–73.

Rahman, S, Majumder, MA, Shaban, SF, Rahman, N, Ahmed, M, Abdulrahman, KB & D’Souza, UJ 2011, ‘Physician participation in clinical research and trials: issues and approaches’, Advances in Medical Education and Practice, vol. 2, pp. 85–93.

Rupnik, C, Chan, J, Kemp, T, Lackey, G, White, H, Richards, D, Butler-Henderson, K, & Low, S 2017, ‘Understanding the perceived barriers/enablers to Health Information Management professionals undertaking research’, Proceedings of the HIMAA/NCCH 34th National Conference, Health Information Management: Challenging a Changing Landscape, 1–3 November 2017, Cairns, Queensland, pp. 66-67.