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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 Sue Wells,  Janine Bycroft, Ai-Wei Lee, Tim Kenealy, Tania Riddell, Rod Jackson (U of A)  & Paul Roseman and Kate Moodabe, ProCare Health Ltd
Overview   ,[object Object],[object Object],[object Object],[object Object],[object Object]
NZ Cardiovascular statistics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
v v Identifying high risk patients
Our Approach.. i. ‘evidence in’ - to improve practice ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
and ii. ‘evidence out’ to improve practice-relevant evidence  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],to feedback into routine practice
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
Implementation in ProCare ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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
Risk assessment: data
Risk Assessment: results
Risk Management: data
Risk management: Action Plan
Risk management: recommendations
Risk Management: patient information
Questions  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PREDICT Health Provider Study   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Methods substudy-patterns of adoption and use ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results 1: adoption   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results: adoption
Results: adoption 0 0
Results 2: patterns of use over time ,[object Object],[object Object],[object Object],[object Object],[object Object]
Results : patterns of use New guidelines published PREDICT  CVD-Diabetes  released ProCare implementation updated PREDICT
Results 3: frequency of use  (n= 416 GPs) ,[object Object],[object Object],[object Object],[object Object]
Results: frequency of use (GPs) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Results: frequency of use (GPs) ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Results: patterns of use of most frequent GPs (88 GPs)
1. Build up then decline  36/88 GPs (41%)
2. Start high and decline  29/88 GPs (33%)
3. Fairly constant  13/88 GPs (15%)
4. All then nothing  10/88 GPs (11%)
This Study ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
This Study ,[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]

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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