Download - The cost of data quality in EMRs
Ahmad Ghany and Karim Keshavjee
ITCH 2017 Feb 18, 2017Victoria, BC
LINK TO OPEN-ACCESS PAPER:
Ghany A, Keshavjee K. The Cost of Quality in Diabetes. Stud
Health TechnolInform. 2017;234:131-135. PubMed PMID: 28186029.
Introduction
The problem with EMR | EHR data
Approaches to clean data
The case study of diabetes Budget Impact Analysis Results of analysis Recommendations Q & A
Adoption of EMRs | EHRs continues to rise in North America
>80% of primary care physicians in Canada use electronic charting
>80% of office-based physicians in US use electronic health records
EMRs | EHRs have resulted in some improvements to quality of care
Full quality improvement benefits difficult to achieve
E.g., improved management of chronic diseases
”Dirty data” is a major culprit
High quality, “clean” data is neededto achieve the full benefits of EMRs | EHRs
2 approaches to obtaining clean data from EMRs | EHRs
1. Data Discipline (DD)
• Train (or force) users to structure data into EMRs at point of care
• Should result in data being entered in standardized manner
• Places heavy burden on busy healthcare providers to collect high quality data
2. Data Cleansing (DC)
• Healthcare providers continue to enter dirty data into EMRs
• Dirty data coded and cleansed using cleansing algorithms
• Has minimal impact on healthcare providers, but requires effort to ensure data coded and cleansed consistently
Both Data Discipline and Data Cleansing result in clean, high quality data
Clean data allows healthcare providers to: Manage chronic diseases more effectively
More efficiently identify and track chronic disease patients
Identify patients whose care is sub-optimal
Identify high risk patients earlier
Access accurate information at the point-of-care = better quality of care All of these factors could decrease the costs associated with
chronic diseases
One prevalent chronic disease in Canada is diabetes Affected 2.7 million people in 2010; forecasted to affect 4.2 million
people in Canada by 2020 Considerable costs associated with diabetes
Estimated to have cost healthcare system $12 billion in 2010
Projected to cost $16 billion by 2020 Clean data is needed to more effectively manage diabetes
What are the costs of implementing each of the approaches to clean data?
Budget Impact Analysis (BIA) is an economic assessment method Quantifies the costs of DD or DC to clean up data for the single chronic disease
of diabetes in an EMR | EHR Overview of BIA for Canada
Population = 24,000 Family Physicians in Canada Time horizon = 2 years (approx. time to disseminate DD) Technology mix = management of diabetes using current methods of data entry
into EMRs (includes dirty data) New interventions being tested are DD and DC Target audience = policy makers, healthcare administrators, providers and any
other stakeholders impacted by cost of data quality Easily adapted for the US Market
BIA compared the costs of DD and DC in 4 key area necessary to implement and sustain each approach:
Cost of materials development
Cost of dissemination
Cost of data quality verification
Cost of maintenance
These key areas drive costs related to human resources, technology and software
Cost of materials development DD: Cost of a health informatics expert & clinician to develop training program
DC: Cost of health informatics expert, programmer and clinician to design, program and test DC algorithm software
Cost of dissemination DD:
▪ Cost of recruiting & training trainers
▪ Recruiting clinicians to be trained
▪ Holding seminars
▪ Clinician time to attend training and implement learnings
DC: ▪ Cost of dissemination of software algorithms to EMR vendors & other software providers
Cost of data quality verification DD:
▪ Cost of human resources for data quality verification▪ Cost of onsite visits or remote reviews
DC:▪ Cost of human resources for data quality verification▪ Develop reporting engine into algorithm software
Cost of maintenance DD:
▪ Cost of developing methodology for cleaning data in a new disease ▪ Training trainers then clinicians on new material▪ Updating existing materials and providing refresher courses
DC: ▪ Cost of developing methodology for new diseases▪ Cost of updating existing materials
BIA also provides breakdown of costs related to diabetes Direct costs:
▪ Hospitalization costs▪ Primary care and specialist costs▪ Medication costs
Indirect costs:▪ Mortality costs▪ Long-term disability costs
Data sources for BIA Data estimated for each aspect of implementing DD and DC based on actual costs
obtained from in-the-field experiences of implementing each approach▪ DD – through Continuing Medical Education program from 2007 to 2010▪ DC – through the Canadian Primary Care Sentinel Surveillance Network (CPCSSN)
Data values for costs of diabetes obtained from report (see reference 5)
Data Discipline Data Cleansing
Effort (hours) Cost ($) Effort (hours) Cost ($)
Cost of Content Development
320 $41,150 186 $17,300
Cost of Content Dissemination
315,820 $47,428,000 288,000 $21,612,000
Cost of Data Quality Verification
48,000 $7,200,000 60 $12,000
Cost of Maintenance
72,664 $10,891,380 223 $20,760
Total 436,804 $65.5 M 288,469 $21.6 M
2010 2020 (projected)
Direct Costs $2.4 B $3.8 B
Indirect Costs $9.2 B $12.1 B
Total Costs $11.6 B $15.9 B
There is a strong business case for improving data quality for diabetes management
4 potential options to consider going forward Do nothing and continue to function with quality of data currently
available ▪ Will not cost any additional money to implement a solution
▪ Costs of this option would be manifested in the rising costs of diabetes▪ Poor quality data contributing to projected high cost of diabetes
Implement Data Discipline▪ Costly and time consuming
▪ Requires considerable time from over-burdened and busy providers
Implement Data Cleansing▪ Quicker to implement & spread throughout healthcare system▪ Estimated to cost less
▪ Newer technologies (text mining & natural language processing) could lower costs even more
▪ Requires less resources to maintain▪ Does not fix missing data
▪ Data Discipline would be required where missing data is a problem
Implement combination of Data Discipline and Data Cleansing▪ Would cost tens of million of dollars▪ Could save healthcare system hundreds of millions of dollars▪ Could save patients billions of dollars and add years of disability-free living to their
lives
Clean data is necessary to effectively manage diabetes Could lead to reduction in direct and indirect costs Could lead to better prediction of costs and complications
Possible that clean data may not result in cost reduction of diabetes If method to clean data is too costly If effort required to manage diabetes is too large
Impact of each potential solution needs to be analyzed before implementation
We have begun to look at the potential impact that 2 data cleaning methods could have on the cost of a single chronic disease
Further analyses required to determine impacts of these approaches on the healthcare system as a whole
Cost of quality needs to be considered before policy decisions are made