Patterns of adoption and use of a web-based decision support system for CVD risk assessment and management in primary care...
Overview   <ul><li>Background-why CVD, why decision support </li></ul><ul><li>Study Aim & design </li></ul><ul><li>Methods...
NZ Cardiovascular statistics <ul><ul><ul><li>Leading cause of death & hospital admissions in NZ </li></ul></ul></ul><ul><u...
v v Identifying high risk patients
Our Approach.. i. ‘evidence in’ - to improve practice <ul><li>Web-based CVD risk assessment  and management programme: </l...
and ii. ‘evidence out’ to improve practice-relevant evidence  <ul><li>provide clinicans with  </li></ul><ul><ul><li>moment...
2002    University of Auckland,  New Zealand Guidelines Group,  National Heart Foundation,  clinical team from CMDHB and P...
Implementation in ProCare <ul><li>Enrolled pop ~660,000, ~500 GPs, 450 practice nurses </li></ul><ul><li>integrated with M...
How did PREDICT CVD work? In clinical setting – doctor or practice nurse sees  a patient and decides to assess & manage th...
Risk assessment: data
Risk Assessment: results
Risk Management: data
Risk management: Action Plan
Risk management: recommendations
Risk Management: patient information
Questions  <ul><ul><li>who adopted PREDICT?  </li></ul></ul><ul><ul><li>if they adopted it – how did they use it? </li></u...
PREDICT Health Provider Study   <ul><li>Aim:  describe the patterns of use, barriers, challenges and enablers to the use o...
Methods substudy-patterns of adoption and use <ul><li>Data collection </li></ul><ul><ul><li>ProCare Clinical registry+NZ M...
Results 1: adoption   <ul><li>533 clinicians chose to adopt PREDICT programme </li></ul><ul><ul><li>doctors (416) and nurs...
Results: adoption
Results: adoption 0 0
Results 2: patterns of use over time <ul><li>Aug 2002-Jan 2007  </li></ul><ul><li>45,437 CVD risk assessments were conduct...
Results : patterns of use New guidelines published PREDICT  CVD-Diabetes  released ProCare implementation updated PREDICT
Results 3: frequency of use  (n= 416 GPs) <ul><li>31% (n=129)non-user: less than 5 patients </li></ul><ul><li>23% (n=95) i...
Results: frequency of use (GPs) <ul><li>non-user: less than 5 patients </li></ul><ul><li>infrequent 5-20 patients </li></u...
Results: frequency of use (GPs) <ul><li>Infrequent/non-users- more likely less than 10years since graduation </li></ul><ul...
<ul><li>Proportion of CVD risk assessments conducted – graphed overtime </li></ul><ul><li>classified into 4 types emerged ...
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 <ul><li>Lifecycle of software from first implementation to obselescence </li></ul><ul><li>45,000+ risk assessme...
This Study <ul><li>Financial incentive ?faciltated uptake, little impact on usage </li></ul><ul><li>Payment in retrospect,...
Conclusions <ul><li>PREDICT CVD -1st of its kind in NZ </li></ul><ul><li>This study -some insights into adoption & usage o...
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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

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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. 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
    2. 2. Overview <ul><li>Background-why CVD, why decision support </li></ul><ul><li>Study Aim & design </li></ul><ul><li>Methods </li></ul><ul><li>Results </li></ul><ul><li>Conclusion </li></ul>
    3. 3. NZ Cardiovascular statistics <ul><ul><ul><li>Leading cause of death & hospital admissions in NZ </li></ul></ul></ul><ul><ul><ul><li>9,000 deaths from CVD each year </li></ul></ul></ul><ul><ul><ul><li>Approx 30% under the age of 70 yrs </li></ul></ul></ul><ul><ul><ul><li>500 deaths on the roads, </li></ul></ul></ul><ul><ul><ul><li>600 deaths from breast cancer </li></ul></ul></ul><ul><ul><ul><li>large health disparities by ethnicity and deprivation </li></ul></ul></ul>
    4. 4. v v Identifying high risk patients
    5. 5. Our Approach.. i. ‘evidence in’ - to improve practice <ul><li>Web-based CVD risk assessment and management programme: </li></ul><ul><ul><li>Fast (moment of care) </li></ul></ul><ul><ul><li>User Friendly </li></ul></ul><ul><ul><li>Integrated with patient electronic record </li></ul></ul><ul><ul><li>Guideline and evidence based </li></ul></ul><ul><ul><li>Individualised recommendations for each patient </li></ul></ul>
    6. 6. and ii. ‘evidence out’ to improve practice-relevant evidence <ul><li>provide clinicans with </li></ul><ul><ul><li>moment of care, </li></ul></ul><ul><ul><li>patient-specific evidence-based advice on assessment and management CVD and Diabetes </li></ul></ul><ul><ul><li>using web-based technology </li></ul></ul><ul><li>simultaneously generate population evidence for </li></ul><ul><ul><li>evaluating health needs, </li></ul></ul><ul><ul><li>assessing health care </li></ul></ul><ul><ul><li>improving the accuracy of risk prediction </li></ul></ul>to feedback into routine practice
    7. 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
    8. 8. Implementation in ProCare <ul><li>Enrolled pop ~660,000, ~500 GPs, 450 practice nurses </li></ul><ul><li>integrated with MedTech, Next Gen </li></ul><ul><li>educational seminars to doctors </li></ul><ul><li>voluntary participation </li></ul><ul><li>target population according to national guidelines </li></ul><ul><li>incentive payment $900/GP after 90 patients </li></ul><ul><li>practice IT facilitators installed software, integration mapping, safe connectivity, one-off training </li></ul>
    9. 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
    10. 10. Risk assessment: data
    11. 11. Risk Assessment: results
    12. 12. Risk Management: data
    13. 13. Risk management: Action Plan
    14. 14. Risk management: recommendations
    15. 15. Risk Management: patient information
    16. 16. Questions <ul><ul><li>who adopted PREDICT? </li></ul></ul><ul><ul><li>if they adopted it – how did they use it? </li></ul></ul><ul><ul><li>any significant differences between user groups? </li></ul></ul><ul><ul><li>what are the most common barriers & challenges to using PREDICT? </li></ul></ul><ul><ul><li>what factors enable and support practices to start or use PREDICT more? </li></ul></ul><ul><ul><li>popular images of power users and non-users – true or myths? </li></ul></ul>
    17. 17. PREDICT Health Provider Study <ul><li>Aim: describe the patterns of use, barriers, challenges and enablers to the use of PREDICT by primary healthcare providers </li></ul><ul><li>3-part qualitative/quantitative evaluation </li></ul><ul><li>key informant interviews ProCare staff & focus groups (GPs & nurses) </li></ul><ul><li>Questionnaire GPs/nurses at CME/CNE group meetings (May 2007) </li></ul><ul><li>descriptive quantitative data analysis </li></ul><ul><ul><li>Adopters and non-adopters </li></ul></ul><ul><ul><li>patterns of use over time, </li></ul></ul><ul><ul><li>characteristics of users </li></ul></ul>
    18. 18. Methods substudy-patterns of adoption and use <ul><li>Data collection </li></ul><ul><ul><li>ProCare Clinical registry+NZ Medical Council data </li></ul></ul><ul><ul><ul><li>Personal and practice names removed, </li></ul></ul></ul><ul><ul><ul><li>NZMC/NZNC number retained </li></ul></ul></ul><ul><ul><li>PREDICT usage data </li></ul></ul><ul><ul><ul><li>Time and date of PREDICT usage </li></ul></ul></ul><ul><ul><ul><li>NZMC or NZNC of user </li></ul></ul></ul><ul><li>Data Linkage- Registry + PREDICT usage data </li></ul><ul><ul><li>Ethical Approval March 2007 Northern Region Ethics Committee </li></ul></ul>
    19. 19. Results 1: adoption <ul><li>533 clinicians chose to adopt PREDICT programme </li></ul><ul><ul><li>doctors (416) and nurses(117) </li></ul></ul><ul><li>607 clinicians did not adopt </li></ul><ul><ul><li>doctors (289) and nurses (318) </li></ul></ul><ul><li>75% who did not adopt had compatible patient management systems </li></ul><ul><li>No differences in adoption by gender,country of medical degree </li></ul><ul><li>More likely to have vocational registration, and be less than 20yrs since graduation </li></ul><ul><li>incomplete practice data on 20% doctors and over 25% nurses (funding, location, size of practice) </li></ul>
    20. 20. Results: adoption
    21. 21. Results: adoption 0 0
    22. 22. Results 2: patterns of use over time <ul><li>Aug 2002-Jan 2007 </li></ul><ul><li>45,437 CVD risk assessments were conducted on 25, 705 patients </li></ul><ul><li>416 GPs and 117 nurses </li></ul><ul><li>Used multiple times within one consultation </li></ul><ul><li>Used subsequent follow-up </li></ul>
    23. 23. Results : patterns of use New guidelines published PREDICT CVD-Diabetes released ProCare implementation updated PREDICT
    24. 24. Results 3: frequency of use (n= 416 GPs) <ul><li>31% (n=129)non-user: less than 5 patients </li></ul><ul><li>23% (n=95) infrequent 5-20 patients </li></ul><ul><li>25% (n=104) frequent user 21-89 patients </li></ul><ul><li>21% ( n=88) most frequent (90+ patients) </li></ul>
    25. 25. Results: frequency of use (GPs) <ul><li>non-user: less than 5 patients </li></ul><ul><li>infrequent 5-20 patients </li></ul><ul><li>compared to </li></ul><ul><li>frequent user 21-89 patients </li></ul><ul><li>most frequent user (90+ patients) </li></ul>
    26. 26. Results: frequency of use (GPs) <ul><li>Infrequent/non-users- more likely less than 10years since graduation </li></ul><ul><li>No difference by gender, country of medical degree </li></ul><ul><li>Older docs (graduated 30yrs+) were as likely to be in either group </li></ul><ul><li>Frequent/most frequent-more likely to have vocational registration </li></ul><ul><li>Unable to adjust for FTE </li></ul>
    27. 27. <ul><li>Proportion of CVD risk assessments conducted – graphed overtime </li></ul><ul><li>classified into 4 types emerged </li></ul><ul><ul><li>Build up then decline </li></ul></ul><ul><ul><li>Start high then decline </li></ul></ul><ul><ul><li>Constant </li></ul></ul><ul><ul><li>All then nothing </li></ul></ul>Results: patterns of use of most frequent GPs (88 GPs)
    28. 28. 1. Build up then decline 36/88 GPs (41%)
    29. 29. 2. Start high and decline 29/88 GPs (33%)
    30. 30. 3. Fairly constant 13/88 GPs (15%)
    31. 31. 4. All then nothing 10/88 GPs (11%)
    32. 32. This Study <ul><li>Lifecycle of software from first implementation to obselescence </li></ul><ul><li>45,000+ risk assessments, ~26,000 patients </li></ul><ul><li>92% usage by doctors </li></ul><ul><li>Large variation in frequency of use </li></ul><ul><li>No differences by gender or country of medical degree </li></ul><ul><li>Differences found by having vocation registration and year of graduation may relect time spent working in practice </li></ul><ul><li>Data limitations </li></ul>
    33. 33. This Study <ul><li>Financial incentive ?faciltated uptake, little impact on usage </li></ul><ul><li>Payment in retrospect, no feedback on progress to target </li></ul><ul><li>Adoption responsive to annual promotional activites via cell groups </li></ul><ul><li>Distinct patterns of use may inform interventions for uptake and to maintain sustainable use </li></ul>
    34. 34. Conclusions <ul><li>PREDICT CVD -1st of its kind in NZ </li></ul><ul><li>This study -some insights into adoption & usage of decision support technology in general practice </li></ul><ul><li>qualitative analyses and questionnaire results more in-depth understanding for future interventions to improve uptake </li></ul><ul><li>Learnings/feedback from PREDICT-CVD applied to new module & implementation package </li></ul><ul><li>Currently 1000+ risk assessments/month with CVD-Diabetes </li></ul>

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