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The Art of the Possible Using CPCSSN Data for Primary Care Research Family Medicine Forum Nov 16, 2012 Karim Keshavjee - EMR Consultant & Research Data.

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Presentation on theme: "The Art of the Possible Using CPCSSN Data for Primary Care Research Family Medicine Forum Nov 16, 2012 Karim Keshavjee - EMR Consultant & Research Data."— Presentation transcript:

1 The Art of the Possible Using CPCSSN Data for Primary Care Research Family Medicine Forum Nov 16, 2012 Karim Keshavjee - EMR Consultant & Research Data Architect Ken Martin - Information and Technology Manager

2 Outline Introduction to CPCSSN CPCSSN Data Holdings A Tour of CPCSSN Data Tables Current Research Projects at CPCSSN The Art of the Possible How to use CPCSSN data for your research Goodies for Today

3 329 physicians in 8 provinces using 10 EMRs 10 PC-PBRNs British Columbia - BCPCReN (Wolf ) Alberta - SaPCReN, Calgary (Med Access, Wolf) - AFRPN, Edmonton (Med Access) Manitoba - MaPCReN, Winnipeg (Jonoke) Ontario - DELPHI, London (Healthscreen, Optimed, OSCAR - NorTReN, Toronto (Nightingale, xwave, Practice Solutions) - CSPC, Kingston (P&P, OSCAR, xwave) Quebec - Q-Net, Montréal (Da Vinci, Purkinje) Nova Scotia / New Brunswick - MarNet, Halifax (Nightingale, Purkinje) Newfoundland - APBRN, St. John’s (Wolf, Nightingale)

4 CPCSSN population CPCSSN Population Data Extracted on all patients in the practice, including children Studying patients with the following chronic diseases Chronic Obstructive Lung Disease Depression Diabetes Hypertension Osteoarthritis Chronic Neurological Disease Dementia Epilepsy Parkinson's Disease

5 Data Holdings Q

6 6 Database Schema - ERD

7 Data Cleaning/Recoding We clean and recode the following fields Billing, Encounter and Problem List Diagnoses (ICD9) Medications (ATC) Lab results (LOINC) Referrals (SNOMED CT) Physical signs (Wt, Ht, BP, unit conversion, calculate BMI) Vaccines (ATC) Risk factors (smoking, alcohol, diet --Text) 7

8 8 Patient Demographics } < 5% 368,000 Records

9 Provider Information 9

10 Billing Million Records Dates of Encounter Original diagnosis sent for billing Text from Code Recoded by CPCSSN Original Diagnosis Code sent for billing Recoded by CPCSSN

11 Research Discussion Useful for case finding Useful for understanding deficiencies of using billing information for clinical research There is some inconsistency in use of billing codes across the country CPCSSN recodes all billing diagnosis codes to a standard version 11

12 Encounters Million Records Dates of Encounter Data inconsistent across the Country CPCSSN Cleaning Not Started Active area of Cleaning E.g., Office Visit, Phone, etc Active area of Cleaning E.g., Office Visit, Phone, etc

13 Research Discussion Can we segment patients by pattern of visits? Does pattern of visits predict other things? – Control of disease – Frequency of prescriptions – Multiple comorbidities Does visit type affect quality of care? Reason for Encounter is poorly captured in most EMRs 13

14 Problem List Diagnoses 14 Original Text Original Diagnosis Written by User E.g. DMT2 Original Diagnosis Written by User E.g. DMT2 Recoded by CPCSSN E.g., Diabetes Mellitus, Type 2 Recoded by CPCSSN E.g., Diabetes Mellitus, Type 2 } Not well populated 1.8 Million Records Active = Problem List Inactive = Past Medical History Active = Problem List Inactive = Past Medical History

15 Problem List Diagnoses 15 List of cleaned up diagnoses Chronic airway obstruction, not elsewhere classified (496) Bronchitis, not specified as acute or chronic (490) Chronic bronchitis (491) Emphysema (492) Diabetes mellitus (250) Depressive disorder, not elsewhere classified (311) Suicide and self-inflicted poisoning by solid or liquid substances (E590) Suicidal ideation (V62.84) Adjustment reaction (309) Post traumatic stress disorder (309.81) Major depressive disorder, recurrent episode (296.3) Bipolar I disorder, most recent episode (or current) (296.7) Mental disorders complicating pregnancy, childbirth, or the puerperium (648.4) Essential hypertension (401) Osteoarthrosis and allied disorders (715) Spondylosis and allied disorders (721) Total knee replacement (81.54) Total hip replacement (81.51) Polycystic ovarian syndrome (256.4) Abnormal glucose tolerance of mother complicating pregnancy childbirth or the puerperium (648.8) Secondary diabetes mellitus (249) MORE BEING ADDED SOON Other abnormal glucose (790.29) Migraine (346) Heart failure (428) Acute myocardial infarction (410) Old myocardial infarction (412) Other forms of chronic ischemic heart disease (414) Cardiac dysrhythmias (427) Essential and other specified forms of tremor (333.1) Esophageal varices with bleeding (456.0) Esophageal varices without bleeding (456.1) Angina pectoris (413) Other acute and subacute forms of ischemic heart disease (411) Calculus of kidney and ureter (592) Portal hypertension (572.3) Asthma (493) Dementias (290) Alzheimer's disease (331.0) Dementia with lewy bodies (331.82) Parkinson's disease (332) Epilepsy and recurrent seizures (345) Epileptic convulsions, fits, or seizures nos (345.9)

16 Research Discussion Sensitivity and specificity of problem list diagnoses not currently known, so cannot determine incidence and prevalence of disease from problem list alone Need to develop case finding criteria for diseases (includes diagnosis, meds, labs, etc) Need to identify sensitivity and specificity of having a diagnosis in the problem list Currently in the process of validating 8 case finding criteria across the country 16

17 Vital Signs 17 Name of exam (e.g., sBP) Cleaned up result (e.g, lbs -> kg, inch -> cm) Cleaned up result (e.g, lbs -> kg, inch -> cm) 5 Million Records Cleaned up unit of measure (e.g., unit is kg, but result was lb) Cleaned up unit of measure (e.g., unit is kg, but result was lb)

18 Research Discussion Currently have access to – sBP/dBP – Ht – Wt – BMI – Waist circum 18

19 Allergies 19 Name of allergen Cleaned up name 155K Records Data will be coded as ATC

20 Research Discussion Not yet cleaned, but will soon clean it Focus of cleaning will be on medication allergies – All other allergies will be retained as original text Useful when assessing why patients are not receiving medications for a particular disease 20

21 Risk Factors 21 Name of Risk Factor (e.g., smoking) Cleaned up version of Risk Factors. 588K Records Working on cleaning up Current Exposures & Cumulative Exposures

22 Research Discussion Risk factors are actively being cleaned Getting the status of the risk factor (i.e., smoker/non-smoker) is difficult, but easier than Current levels of exposure (e.g., # of cig/day) Cumulative exposure (e.g., pack years) Alcohol use is also being cleaned up 22

23 Laboratory Results 23 Original Lab Result Name (e.g., Hb A1c, HGbA1c, etc) Original Lab Result Name (e.g., Hb A1c, HGbA1c, etc) Recoded by CPCSSN 100% LOINC (e.g., HBA1C) Recoded by CPCSSN 100% LOINC (e.g., HBA1C) 3 Million Records

24 Research Discussion Currently only capturing the following One site does not capture labs yet 24 HDL TRIGLYCERIDES LDL TOTAL CHOLESTEROL FASTING GLUCOSE HBA1C URINE ALBUMIN CREATININE RATIO MICROALBUMIN GLUCOSE TOLERANCE

25 Encounter Diagnoses 25 Original Diagnosis Recorded in Encounter (e.g., axniety) Original Diagnosis Recorded in Encounter (e.g., axniety) 83% Recoded by CPCSSN (Anxiety ICD-9 300) 83% Recoded by CPCSSN (Anxiety ICD-9 300) 6.3 Million Records 63% Originally coded by Doctor

26 Research Discussion Not all EMRs capture Encounter Diagnoses in a structured manner This table is not ready for prime time across all sites, but may be useful for projects where data from just a few sites is acceptable 26

27 Medications 27 What the doctor ordered E.g., HCTZ 25 mg bid What the doctor ordered E.g., HCTZ 25 mg bid 91% Recoded by CPCSSN E.g., Hydrochlorthiazide 91% Recoded by CPCSSN E.g., Hydrochlorthiazide 56% Coded as DIN Strength 56% Dose 70% Unit of Measure 84% Frequency 95% Duration 52% Dispensed 86% Strength 56% Dose 70% Unit of Measure 84% Frequency 95% Duration 52% Dispensed 86% 72% Coded by doctor (DIN + other) 91% Coded by CPCSSN (ATC) 4.9 Million Records }

28 Research Discussion Medication name data is relatively clean Medications coded as ATC – Allows easy grouping by class Don’t have daily dose and months supply for many records –working on clean up 28

29 Referrals 29 Original Text of Referral 80% Recoded by CPCSSN SNOMED-CT 80% Recoded by CPCSSN SNOMED-CT 600 K Records

30 Procedures 30 Original Text of Procedure Not Currently Coded by CPCSSN 1.3 Million Records

31 Vaccines 31 What the doctor typed 93% Recoded by CPCSSN (ATC) 960 K Records 46% Coded by Doctor (DIN)

32 Disease Cases ,000 Records Case Definitions are developed by CPCSSN and are in the process of being validated through chart reviews How a Case is identified is recorded in this table Allows full traceability for each case

33 Current Research Projects at CPCSSN N=46 Association Study9% Attitudes2% Audit and feedback2% Case control study7% Case Finding9% Clinical Quality Improvement2% Continuity of Care2% Data Quality20% De-identification2% Denominator2% Descriptive Study2% EMR Adoption2% Feasibility2% Intervention Assessment2% Medication2% Practice Profile4% Prevalence7% Prevalence, Case finding2% Resource Use7% SES Study4% Treatment pattern4% Validation4%

34 Research Opportunities Population Health and Epidemiological Studies – Incidence/Prevalence of disease – Impact of SES on health – Rates of treatment for diseases – Rates of disease control – Burden of illness and multi-morbidity Clinical –database studies – Comparative effectiveness – Case-Control – Exposure-Outcome – Quality Improvement – Associations – Intervention-Outcome – Guideline effectiveness 34

35 Research Opportunities Clinical –prospective, interventional studies – Conduct pragmatic RCTs –data is already collected – Conduct in-clinic interventions – Not ready for these yet Health Services – EMR adoption – Resource Utilization (consults, labs, procedures) – Policy Intervention (cross-province comparisons) – Patient behaviors –frequency of visits – Medical errors and patient safety 35

36 Research Opportunities Health informatics – Natural language processing – Machine learning – De-identification algorithms – Predictive Analytics eHealth and mHealth – Develop and test apps using CPCSSN data – Patient education apps with their own data – Apps for healthcare providers to educate patients about their disease with nice visualizations 36

37 Research Using CPCSSN Data 37 Researcher Letter of Intent CPCSSN Research Committee Writes Letter of Intent Reviews 1 page, includes: Researchers, Organization, Research Title, Objective, Methodology, Data Required 1 page, includes: Researchers, Organization, Research Title, Objective, Methodology, Data Required Approved 1. Resubmit 2. Not Feasible 3. Outside Mandate 1. Resubmit 2. Not Feasible 3. Outside Mandate No Researcher 1. Protocol 2. Data Access Request Form 3. Data Sharing Agreement 1. Protocol 2. Data Access Request Form 3. Data Sharing Agreement Letter of Acceptance Yes Writes CPCSSN Research Committee CPCSSN Data Researcher Invoice

38 Goodies For Today Copy of the presentation: The Art of the Possible : Using CPCSSN Data for Primary Care Research Sample of CPCSSN data for 200 patients – Anonymized and scrambled to protect patient privacy – (MS Access file format) CPCSSN database entity relationship diagram (ERD) CPCSSN database data dictionary CPCSSN central repository data holdings summary CPCSSN Data Access Request Form Central Repository Process for Requesting Access to CPCSSN Data 38

39 Next Steps Sign a License Agreement today to get your copy of the CPCSSN Data Product Evaluate the data CPCSSN has Plan your next grant application around CPCSSN data Add CPCSSN Data as a budget item into your next grant application – You can contact us to get a quote 39

40 Contact Tyler Williamson, Senior Epidemiologist Canadian Primary Care Sentinel Surveillance Network Centre for Studies in Primary Care Queen’s University Kingston ON K7L 5E9 Tel: (613) , Ext Fax: (613)

41

42 Thanks to all Funders, Stakeholders, Partners, AND sentinel Physicians 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. 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.


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