2. Routinely-collected data from GPs’ EPR and GP active electronic questioning method: a comparative study ACHIL research laboratory Etienne De Clercq (UCL-IRSS), V. Van Casteren, S. Moreels, N. Bossuyt, Katrien Vanthomme (ISP), G. Goderis, (KUL) (Etienne.DeClercq@UCLouvain.be) MEDINFO 2013 Congress Copenhagen 20/08/13 – 23/08/13 Etienne De Clercq: Clos Chapelle aux Champs 30 Bte B1.30.13 | 1200 Brussels | Belgium | T +32 2 764.32.62 | email: etienne.declercq@UClouvain.be UCLouvainKULeuven Ambulatory Care Health Information Laboratory ACHIL ACHIL is funded by the National Institute for Health and Disability Insurance

3. 2 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Theoretical framework Patients healthcare status (PHS) GP’s ThoughtsEPR Research DB Q Questionnaire Research DB AE PHS as perceived by the GP Documented PHS Proxy

4. 3 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Research questions Is there any agreement between both research DB? Could “PHS as perceived by the GPs” be deduced from the “documented PHS” (aggregated data)? Is it useful to perform both data collection methods at the same time?

5. 4 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain ResoPrim data collection GPs’ consultationEPRResearch DB AE Research DB Q Questionnaire Source validation

6. 5 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Clinical automatic extracted data  New coded and active diagnosis (ICPC2, ICD10, Belgian Thesaurus) (hypertension, diabetes type 2, cardiovascular past event)  New coded and active drug prescription (ATC code) (anti-diabetic drugs, anti-hypertension drugs, aspirin, statin)  Clinical Parameters (2 most recent values extracted): height, weight, syst. & diast. Blood pressure  Biological Parameters (2 most recent values extracted): Total & LDL cholesterol

7. 6 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Is the patient suffering from hypertension? Does the patient currently take any antihypertensive drugs? Is the patient suffering from type 2 diabetes? Does the patient currently take any antidiabetic drugs? Does the patient have a history of cardiovascular event(s)? Does the patient currently take low-dose aspirin? Is the patient’s blood pressure currently higher than 140/90? Does the patient currently take a statin? Is the patient overweight (BMI > 25)? Patient known to have a history of hypercholesterolemia? (total cholesterol > 190 mg/dl and/or LDL cholesterol > 115 mg/dl) Electronic questionnaire

8. 7 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Electronic questionnaire Is the patient suffering from hypertension? Does the patient currently take any antihypertensive drugs? Is the patient suffering from type 2 diabetes? Does the patient currently take any antidiabetic drugs? Does the patient have a history of cardiovascular event(s)? Does the patient currently take low-dose aspirin? Is the patient’s blood pressure currently higher than 140/90? Does the patient currently take a statin? Is the patient overweight (BMI > 25)? Patient known to have a history of hypercholesterolemia? (total cholesterol > 190 mg/dl and/or LDL cholesterol > 115 mg/dl)

9. 8 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Agreement between research DB (1) Kappa (AE vs Questions) Healthcare conditions Extracted dataMissing excludedMissing AE = “-” Hypercholes- terolemia Cholesterol-0,12-0,06 Cholest. < 4 months -0,060,05 Blood Pressure > 140/90 Blood Pressure0,420,40 BP < 4 months0,470,44 Overweight (BMI > 25) BMI0,440,33 BMI < 4 months0,480,20

10. 9 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Agreement between research DB (2) Kappa (AE vs Questions) Healthcare conditions Extracted dataMissing excludedMissing AE = “-” HypertensionHT Diag. codeN/A0,47 DiabetesDiab. Diag. codeN/A0,55 PCVEPCVE Diag. codeN/A0,36 HT DrugsHT Drug codeN/A0,24 Diab. DrugsDiab. Drug codeN/A0,75 AspirinAspirin Drug codeN/A0,44 StatinStatin Drug codeN/A0,54

11. 10 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Aggregated Patient Healthcare Status proxies – Prevalences (1) Observed AE PrevalenceQ Prevalence Healthcare conditions Extracted data Missing excluded Missing AE = “-” Hypercholes- terolemia Cholesterol65,17%52,4% 45,5% Cholest. < 4 months 60,91%20,5% Blood Pressure > 140/90 Blood Pressure 48,5%45,6% 28 BP < 4 months 49,2%39,6% Overweight (BMI > 25) BMI75,35%47,5% 56,4% BMI < 4 months 78,88%22,1%

12. 11 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Aggregated Patient Healthcare Status proxies – Prevalences (2) Observed AE Prevalence Q Prevalence Healthcare conditions Extracted data Missing excluded Missing AE = “-” HypertensionHT Diag. codeN/A17,4%30,6% DiabetesDiab. Diag. codeN/A5,3%7,5% PCVEPCVE Diag. codeN/A4,7%8,6% HT DrugsHT Drug codeN/A68,1%91,9% Diab. DrugsDiab. Drug codeN/A14,0%26,7% AspirinAspirin Drug codeN/A20,6%42,8% StatinStatin Drug codeN/A22,4%38,3%

13. 12 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain From documented care to a better proxy e.g. PPV: proportion of drug codes (extracted from EPR) confirmed by the GPs’ answers to the questionnaire? e.g. Sensitivity: Proportion of patients with drug prescription (according to the GPs’ answers to the questionnaire) identified by a drug code extracted from EPR?

14. 13 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Estimated Q prevalence (1) Healthcare conditions Extracted data Observed AE Prevalence Q Prevalence Estimated Q Prevalence Hypercholes- terolemia Cholesterol52,4%45,5%45,1% Cholest. < 4 months 20,5%45,5%44,8% Blood Pressure > 140/90 Blood Pressure 45,6%28,0%27,8% BP < 4 months 39,6%28,0%27,7% Overweight (BMI > 25) BMI47,5%56,4%56,1% BMI < 4 months 22,1%56,4%56,2%

15. 14 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Estimated Q prevalence (2) Healthcare conditions Extracted data Observed AE Prevalence Q Prevalence Estimated Q Prevalence HypertensionHT Diag. code17,4%30,6%31,5% DiabetesDiab. Diag. code5,3%7,5%8,0% PCVEPCVE Diag. code4,7%8,6%9,1% HT DrugsHT Drug code68,1%91,9%90,5% Diab. DrugsDiab. Drug code14,0%26,7%19,5% AspirinAspirin Drug code20,6%42,8%42,6% StatinStatin Drug code22,4%38,3%

16. 15 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Documentation impact Prevalence Statin PPVSens.AEQEstimated Year 190.4%52.9%22.39%37.84%38.26% P Est. = P AE * PPV / Sens. Year 235.00%??? 90.4% 84.3% 37.53% Year 2bis28.00%??? 90.4%52.9%47.85%

17. 16 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain Benefits of the approach Documentation process impact “Triangulation” benefits

18. 17 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain “Triangulation” Benefits 3261 patientsRelevant question Extracted parameters+-missing Cholesterol < 4 months +31630351 -21816547 missing8151148198 Identifying potential tracks to improve the quality of care or the quality of the documentation of care

19. 18 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain One message … If we were to use routinely collected data from primary care EPR for secondary usage, such as assessment of quality of care, we strongly advise To try, as far as possible, to identify the impact of the documentation system, or at least to compare with one another data collection process to identify potential ways to improve both care quality and information system.

20. 19 ACHIL, funded by the National Institute for Health and Disability Insurance UCLouvain