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ACHIL research laboratory

Ambulatory Care Health Information Laboratory ACHIL. KULeuven. UCLouvain. ACHIL is funded by the National Institute for Health and Disability Insurance. Routinely-collected data from GPs’ EPR and GP active electronic questioning method: a comparative study. ACHIL research laboratory

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ACHIL research laboratory

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  1. Ambulatory Care Health Information Laboratory ACHIL KULeuven UCLouvain ACHIL is funded by the National Institute for Health and Disability Insurance 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, KatrienVanthomme (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

  2. Theoretical framework DocumentedPHS Research DB AE Proxy Patients healthcare status (PHS) GP’s Thoughts EPR Questionnaire Proxy PHS as perceived by the GP Research DB Q

  3. 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?

  4. GPs’ consultation EPR Research DB AE Research DB Q ResoPrim data collection Questionnaire Source validation

  5. 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

  6. Electronic questionnaire

  7. Agreement between research DB (1)

  8. Agreement between research DB (2)

  9. Aggregated Patient Healthcare Status proxies – Prevalences (1)

  10. Aggregated Patient Healthcare Status proxies – Prevalences(2)

  11. 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?

  12. Estimated Q prevalence (1)

  13. Estimated Q prevalence (2)

  14. 84.3% 90.4% 90.4% 52.9% Documentation impact PEst.= PAE * PPV / Sens. 37.53% 47.85%

  15. Benefits of the approach • Documentation process impact • “Triangulation” benefits

  16. “Triangulation” Benefits Identifying potential tracks to improve the quality of care or the quality of the documentation of care

  17. 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.

  18. That's all ...!

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