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Introducing a Pan-Canadian Surveillance Network:

Introducing a Pan-Canadian Surveillance Network: Using EMRs to Develop a Data Repository for Chronic Diseases and Research in Primary Care Manitoba eHealth Conference, October 2013 Winnipeg, MB Alan Katz, Gayle Halas, Bill Peeler.

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Introducing a Pan-Canadian Surveillance Network:

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  1. Introducing a Pan-Canadian Surveillance Network: Using EMRs to Develop a Data Repository for Chronic Diseases and Research in Primary CareManitoba eHealthConference, October 2013 Winnipeg, MBAlan Katz, Gayle Halas, Bill Peeler Funding for this publication was provided by the Public Health Agency of Canada. The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada.

  2. pan-Canadian • Primary Care • Sentinel Surveillance • comprised of participating practice-based research Networks who use Electronic Medical Records What is CPCSSN?

  3. British Columbia • BCPCReN, Vancouver - Wolf, OSCAR(1) • Alberta • - SAPCReN, Calgary - Med Access, Wolf • - AFPRN, Edmonton - Med Access, Wolf • Manitoba • - MaPCReN, Winnipeg - JonokeMed • Ontario • - DELPHI, London - Healthscreen, Optimed-Accuro, OSCAR • - UTOPIAN, Toronto - Nightingale, Practice Solutions, Bell EMR • - CSPC, Kingston - P&P(4), OSCAR, Bell EMR, Practice Solutions(1), Nightingale(1) • Quebec • - RRSPUM-Réseau de recherche en soins • primaires de l'Université de Montréal  - Da Vinci, Purkinje (2) • Nova Scotia/New Brunswick • - MaRNet, Halifax - Nightingale, Purkinje (3) • Newfoundland • APBRN, St. John's - Wolf, Nightingale CPCSSN consists of: (1) = recuited but not yet operational (2) = nearly operational (3) = available (4) = supported as legacy 10 PBRNs across Canada

  4. Manitoba Primary Care Research Network (MaPCReN) • Located at University of Manitoba, Dept of Family Medicine • Manitoba EMR data extracted from 3 Primary Care Practices (JonokeMed System) • MaPCReN expanding to include practices using Optimed Accuro system. Manitoba component of CPCSSN As of June 30, 2013

  5. Initial Objectives: • Develop an infrastructure for the collection of primary care data from EMRs • Use the data to: • create a searchable data repository • generate reports on selected chronic diseases

  6. Provide estimates of incidence, prevalence, outcomes and healthcare utilization • Provide warning about changes over time of chronic disease in the population • Provide evidence for effectiveness of interventions • Information for government planning • Information for practitioners Why do surveillance of chronic disease?

  7. Longitudinal data from primary care practices • Anonymized and stored in a data repository • Used to assess and report on the epidemiology and management of the following chronic conditions: CPCSSN Data

  8. CPCSSN has created a pan-Canadian protocol • Compliant with all current research protocols and ethics guidelines • Safeguarding research subject privacy while enabling public health surveillance and research Privacy and Ethics Across 10 research networks and 8 different provincial privacy regimes!

  9. Patient Privacy and Data Flow Unique CPCSSN number Practice Sentinel Site Regional Network Central Data Repository Office CPCSSN ‘Key’ Public Health Agency of Canada (PHAC) Canadian Institute for Health Information (CIHI)

  10. Site 4 EMR5 Network 3 Network 1 Network 2 Site 2 EMR 3 Site 4 EMR4 Site 1 EMR3 Site 2 EMR1 Site 3 EMR2 Site 1 EMR1 Site 4 EMR2 Site 1 EMR3 Site 3 EMR5 Site 2 EMR2 Site 3 EMR4 CPCSSN Repository Network Architecture

  11. MaPCReN Data Flow STEP 1: EMR Data is extracted from each participating Sentinel Provider.

  12. MaPCReN Data Flow STEP 2: The extracted data is transferred to the Regional Server.

  13. MaPCReN Data Flow STEP 3: The transferred data is processed using region- specific coding algorithms. This produces one single anonymized Regional database.

  14. MaPCReN Data Flow STEP 4: The regional database is then processed using standardized national coding algorithms which convert Regional information following national standards. Processing includes case detection algorithms.

  15. MaPCReN Data Flow STEP 5: The regional data is then submitted to be merged into a single CPCSSN Central Repository database by the Senior Data Manager.

  16. MaPCReN Data Flow STEP 6: A series of validation and analysis reports are generated from the Central Repository Database.

  17. MaPCReN Data Flow STEP 7: Individual Sentinel Provider Reports are generated and distributed back to each of the Sentinel Providers taking part in the study.

  18. Regional Processing Data Cleaning & Data Standardization • Different EMRs capture data differently • Smoker, current smoker, non-smoker, • ex-smoker • 7% vs 0.07 • ICD9 vs ICDA (Ontario) • Data field mismatch • Medication: Indocid 25 mg bid--is that 2, 3 or 4 fields? • Labs: Na 135 mmol/L (132 -140) N – Normal range could be one field or two

  19. Regional Processing • “Dirty data”(misspellings, extra words in field, inconsistent strings (ex smoker, ex-smoker), multiple diagnoses in a single field) • Can be cleaned by data managers • “Missing data”(dosages, dates of onset, occupation, ethnicity) • Most cannot be fixed, some can be inferred • “Inconsistent data”(Diagnoses stored in different places –notes, PMH, problem list, Inconsistent Risk Factors coexisting –smoker, ex-smoker • Need to find the best source of data for each EMR Data Quality Issues

  20. Regional Processing • “Cloudy data” (referral to X-Ray or Dr. Jones) • Cannot be fixed, other than enter in a pre-formatted manner • “Lacking Meta Data” (Diagnosis not in problem list, Medication in encounter notes) • Cannot be fixed, other than enter pre-formatted • “Lacking Standardization” (multiple, changing, inconsistent names or results for lab tests –HbA1C, glycosylated hemoglobin, 7% vs. 0.07 for test results) • Must be fixed by national lab standards • “Lacking data feeds” (lab results not coming in electronically) • Needs to be fixed at clinic level Data Quality Issues

  21. Allergies Billing Case Detection Examinations Health Conditions Lab Tests Medical Procedures Prescriptions Referrals Risk Factors Vaccinations Visits

  22. Allergies Disease Case Indicators Billing Case Detection Examinations Health Conditions Lab Tests Medical Procedures Prescriptions Referrals Risk Factors Vaccinations Visits

  23. Allergies Disease Case Indicators Disease Cases Billing Case Detection Examinations Health Conditions Lab Tests Medical Procedures Prescriptions Referrals Risk Factors Vaccinations Visits

  24. Selection Criteria: Diabetes Billing, Health Condition, or Encounter Diagnosis records

  25. Selection Criteria: Diabetes Lab Result records

  26. Selection Criteria: Diabetes Medication records

  27. Case Detection of Diabetes Patient

  28. Manitoba Chart Audit/Validation Comparing diagnoses: CPCSSN algorithm vs. chart reviewer Seeking to characterize the source of any discrepancies in 5 of the conditions 403 patient records reviewed using a standardized abstraction form WHAT Work In Progress WHY

  29. Patients by 10-year Age Groups Total Patients (10 year age groups) Disease Prevalence (10 year age groups)

  30. Diabetes Mellitus Prevalence – All Age Groups

  31. Diabetes Mellitus Comorbidity

  32. Diabetes Mellitus Comorbidity

  33. Lessons Learned • Governance –pan-Canadian organization • Willingness – patients, primary care providers (physician and NPs) • Complexity of “translating” the data: • Different EMRS • Different users • Processes that work… continuous evaluation

  34. Opportunities and Possibilities A national surveillance system for chronic disease, --never been available before Contributing to primary care research capacity: incidence prevalence outcomes and healthcare utilization longitudinal changes in chronic disease More complex data extraction processes

  35. Project Achievements Initial development: infrastructure and IT architecture Effective collaboration: multiple provinces multiple university research centres multiple primary care clinics Manitoba EMR data: successfully and securely transferred stored at a central data repository

  36. CPCSSN Phase I, II, and III CPCSSN’s primary partner is the College of Family Physicians Canada (CFPC) along with DFM practice based research networks (PBRNs) across Canada May 2008- April 2010Funders:Public Health Agency of Canada pilot and feasibility phases I & II plusa 5 yr contribution agreement between CFPC & PHAC for Phase III May 2010 – March 2015Additional Partner:Canadian Institute for Health Information (CIHI)

  37. Thanks to all funders, stakeholders, partners AND sentinel physicians Funding for this publication was provided by the Public Health Agency of Canada The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada. Cette publication a été réalisée grâce au financement de l'Agence de la santé publique du Canada. Les opinions exprimées ici ne reflètent pas nécessairement celles de l'Agence de la santé publique du Canada.

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