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Data Quality in the WRHA: Is our data reliable?

Data Quality in the WRHA: Is our data reliable?. Dr. Alexander Singer and Sari Yakubovich. October 30, 2103. Outline. Project Objectives What is data quality? Why do we care? The WRHA Research Database and Queries How do we measure data quality? Preliminary Clinic Data Limitations

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Data Quality in the WRHA: Is our data reliable?

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  1. Data Quality in the WRHA: Is our data reliable? Dr. Alexander Singer and Sari Yakubovich October 30, 2103

  2. Outline • Project Objectives • What is data quality? • Why do we care? • The WRHA Research Database and Queries • How do we measure data quality? • Preliminary Clinic Data • Limitations • Future Directions • References October 30, 2103

  3. Objectives • To assess the baseline EMR data quality in the WRHA's primary care network. • To gain a better understanding of how primary care providers in Winnipeg utilize their EMR. • To understand the factors which contribute quality data. • To create potential interventions to facilitate improvement. • To share our findings and establish connections with other EMR researchers in Canada. October 30, 2103

  4. What is Data Quality? • Very elusive term • In this context = “data fit for purpose” (Bowen and Lau, 2012) • Does the data being entered into the EMR database accurately represent what we think it should? October 30, 2103

  5. An Example • Patients billed for a diabetes visit should also have diabetes recorded in their problem list October 30, 2103

  6. Why Do We Care? • Data quality = better way to measure how effectively the EMR is being used • We don’t just want electronic paper charts • The goal is to create an interoperable system October 30, 2103

  7. Patient Portal Primary Care Providers Pharmacy If data quality is poor there could be severe consequences Specialists Interoperable EMR Labs October 30, 2103 Billing Hospital EMR

  8. October 30, 2103 Dr. Morgan Price, Knowledge Translation Community Session 16, March 6, 2013

  9. The WRHA Research Database • WRHA EMR = shared database • As we receive consents, we get full access to the clinics • Research database • Mirror of production • Frozen in time at June 18, 2013 • Queries are restricted to 18 months prior to that date October 30, 2103

  10. Queries • Create queries in the research database using EMR query builder • Run reports; collect aggregate data • Record results in data capture Excel spreadsheet October 30, 2103

  11. October 30, 2103

  12. How Do We Measure Data Quality? • UVic EMR Data Quality Evaluation Guide • Parameters include: • Demographics and Panels • Consistency of Capture • Concordance • Correctness • Completeness • Currency • All data analysis has been restricted to activity 18 months prior to June 18, 2013 October 30, 2103

  13. Demographics and Panels • Example: Practice/clinic populations • Why: Differences in practice populations may lead to differences in disease prevalence • Result: Errors in “panel correctness” at all clinics owing to the shared WRHA database; errors ranged from 4 to 553 patient records. October 30, 2103

  14. Panel Sizes October 30, 2103

  15. WRHA Clinic Practice Populations October 30, 2103

  16. October 30, 2103

  17. October 30, 2103

  18. Continuity of Care - Clinic A October 30, 2103

  19. Correctness • Does the data that should not deviate from certain values (e.g. gender), accurately represent what it should? • Example: How many patients have a record of a PSA test and a female gender recorded? • Why: To determine if these data elements are incorrectly recorded, indicating error • Result: Most clinics did not have any errors in correctness; however PSA and PAP test records were low or non-existent for some clinics. October 30, 2103

  20. Consistency of Capture • Are data elements being consistently recorded ? • Example: How many patients seen in the past 18 months have at least one problem recorded in their problem list? • Why: Need to understand the extent of capture before further assessment • Result: The % of active patients in WRHA clinics that have at least one entry in their problem list ranges from 56-81%, with an average of 65%. October 30, 2103

  21. Consistency of Capture -Prescriptions October 30, 2103 Data Quality Goal= >60%

  22. Consistency of Capture – Social Determinants of Health October 30, 2103

  23. Concordance • Is the data in relative agreement with other reliable sources? • Example: Prevalence of diabetes within the clinic or practice compared to the expected prevalence • Why: If there is significant deviation from expected results, could mean unreliable data • Results: Prevalence of Diabetes in these WRHA clinics is on average 7.6%. According to Stats Canada 6.2% of Manitobans, and 6.5% of Canadians have Diabetes. October 30, 2103

  24. Concordance - Hypertension Data Quality Goal= ~16.7% (MB) - 17.4% (Canada) http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/health70b-eng.htm October 30, 2103

  25. WRHA Concordance Summary October 30, 2103

  26. Completeness • Is the data free of gaps that may limit its ability to represent the “true state of affairs”? • Example: How many patients who have been billed for hypothyroidism in the past 18 months also have it recorded in their problem list? • Why: If there are significant gaps in data, it’s not an accurate representation • Result: In WRHA clinics 62-80% of patients billed for a hypothyroidism visit also have the diagnosis in their problem list, with an average of 72%. October 30, 2103

  27. Completeness - Problem List vs. Billing (Hypertension) Data Quality Goal= 100% October 30, 2103

  28. Completeness- BP Record vs. Billing (Hypertension) October 30, 2103 Data Quality Goal= 100%

  29. Completeness- Problem List vs. Billing (Diabetes) Data Quality Goal= 100% October 30, 2103

  30. Completeness - Problem List vs. Billing (Asthma) October 30, 2103 Data Quality Goal = 100%

  31. Completeness - Problem List vs. Billing (summary) • COPD: 48.55% • CHF: 49.49% • CAD: 67.10% October 30, 2103

  32. Completeness - Problem List vs. Medications Hyperlipidemia vs. Statin Data Quality Goal= 100% October 30, 2103

  33. Completeness - Hypothyroidism vs. Levothyroxine Data Quality Goal = 100% October 30, 2103

  34. Completeness - Diabetes vs. Insulin/PO Hypoglycemic (excluding metformin) Data Quality Goal= 100% October 30, 2103

  35. Completeness - Insomnia vs. Zopiclone Data Quality Goal= 100% October 30, 2103

  36. Completeness – Problem List vs. Medications • Triptans/Migraine: 51.33% • Allopurinol/Gout: 53.79% • Spiriva/COPD: 61.01% • Bisphosphonates/Osteoperosis, Paget’s Disease: 62.69% • Donepezil/Alzheimer’s Disease, Dementia: 76.01% • Warfarin/Dx Exists: 93.28% October 30, 2103

  37. Currency • Are routine measures such as, BP, height, and weight being collected and recorded? • Example: Of the patients seen in the past 18 months how many of them have BPs recorded within that time frame? • Why: It is important to use recent figures and if these are not being recorded properly it’s possible that other measures also aren’t October 30, 2103

  38. WHRA Currency Results • BP recorded past 18 months: 45.14% • Height recorded past 18 months: 48.47% • Weight recorded past 18 months: 54.14% October 30, 2103

  39. Limitations • Limited by the query builder in Accuro • For example; cannot assess consistency of form • Allergies are difficult to report on • Database is frozen in time as of June 18, 2013 October 30, 2103

  40. Future Directions • Continue to analyze and collect data from WRHA clinics • Collect data from non-WRHA clinics to compare data quality • Interventions to improve data quality October 30, 2103

  41. References Bowen, M. (2012). EMR Data Quality Evaluation Guide. Retrieved from http://ehealth.uvic.ca/resources/tools/EMRsystem/EMRsystem.php Bowen, M. & Lau, F. (2012). Defining and evaluating electronic medical record data quality within the Canadian context. Electronic Healthcare, 11(1), e5-e13 Greiver, M., Barnsley, J., Aliazadeh, B., Krueger, P., Moineddin, R., Butt, D.A., Dolabchian, E., Jaakkimainen, L., Keshavjee, K., White, D., & Kaplan, D. (2011). Using a data entry clerk to improve data quality in primary care electronic medical records: a pilot study. Informatics in Primary Care, 19, 241-250 The College of Family Physicians of Canada. (2012). Best Advice-Panel Size. Retrieved from http://www.cfpc.ca/Best_Advice_Panel_Size/ October 30, 2103

  42. Thank you for listening! October 30, 2103

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