Patterns of adoption and use of a web-based decision support system for CVD risk assessment and management in primary care
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Sue Wells, Janine Bycroft, Ai-Wei Lee, Tim Kenealy, Tania Riddell, Rod Jackson (U of A) & Paul Roseman and Kate Moodabe, ProCare Health Ltd
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Patterns of adoption and use of a web-based decision support system for CVD risk assessment and management in primary care
- 1. Patterns of adoption and use of a web-based decision support system for CVD risk assessment and management in primary care Sue Wells, Janine Bycroft, Ai-Wei Lee, Tim Kenealy, Tania Riddell, Rod Jackson (U of A) & Paul Roseman and Kate Moodabe, ProCare Health Ltd
- 4. v v Identifying high risk patients
- 7. 2002 University of Auckland, New Zealand Guidelines Group, National Heart Foundation, clinical team from CMDHB and ProCare, IT team from Enigma Implementation: 2002 ProCare Prompt (opportunistic RA & management) 2003 CMDHB Chronic disease management module 2004 Middlemore Hospital Coronary Care Unit & Medical Outpatients PREDICT-CVD
- 9. How did PREDICT CVD work? In clinical setting – doctor or practice nurse sees a patient and decides to assess & manage their CVD risk Patient-practitioner interaction
- 13. Risk management: Action Plan
- 15. Risk Management: patient information
- 21. Results: adoption 0 0
- 23. Results : patterns of use New guidelines published PREDICT CVD-Diabetes released ProCare implementation updated PREDICT
- 28. 1. Build up then decline 36/88 GPs (41%)
- 29. 2. Start high and decline 29/88 GPs (33%)
- 30. 3. Fairly constant 13/88 GPs (15%)
- 31. 4. All then nothing 10/88 GPs (11%)