1) The document analyzes the costs of two approaches to obtaining clean data from electronic medical records (EMRs) - data discipline and data cleansing - and applies this to diabetes management in Canada. 2) A budget impact analysis finds that data cleansing would be quicker to implement and estimated to cost less at $21.6 million compared to $65.5 million for data discipline. 3) The analysis recommends considering a combination of the two approaches to improve data quality for diabetes management, which could save hundreds of millions to the healthcare system and billions to patients through reduced costs and improved health.

1. Ahmad Ghany and Karim Keshavjee
ITCH 2017 Feb 18, 2017
Victoria, 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.

2.  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

3.  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

5.  High quality, “clean” data is needed
to 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

6.  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

7.  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?

8.  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

9.  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

10.  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

11.  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

12.  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)

13. 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

14. 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

15.  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

16.  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

17.  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