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The cost of data quality in EMRs


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Presentation made at the ITCH 2017 conference in Victoria.

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The cost of data quality in EMRs

  1. 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. 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. 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
  4. 4.  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
  5. 5.  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
  6. 6.  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?
  7. 7.  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
  8. 8.  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
  9. 9.  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
  10. 10.  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
  11. 11.  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)
  12. 12. 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
  13. 13. 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
  14. 14.  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
  15. 15.  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
  16. 16.  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