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