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 Background-why CVD, why decision support Study Aim & design Methods Results Conclusion

Background-why CVD, why decision support

Study Aim & design

Methods

Results

Conclusion

NZ Cardiovascular statistics Leading cause of death & hospital admissions in NZ 9,000 deaths from CVD each year Approx 30% under the age of 70 yrs 500 deaths on the roads, 600 deaths from breast cancer large health disparities by ethnicity and deprivation

Leading cause of death & hospital admissions in NZ

9,000 deaths from CVD each year

Approx 30% under the age of 70 yrs

500 deaths on the roads,

600 deaths from breast cancer

large health disparities by ethnicity and deprivation

v v Identifying high risk patients

Our Approach.. i. ‘evidence in’ - to improve practice Web-based CVD risk assessment and management programme: Fast (moment of care) User Friendly Integrated with patient electronic record Guideline and evidence based Individualised recommendations for each patient

Web-based CVD risk assessment and management programme:

Fast (moment of care)

User Friendly

Integrated with patient electronic record

Guideline and evidence based

Individualised recommendations for each patient

and ii. ‘evidence out’ to improve practice-relevant evidence provide clinicans with moment of care, patient-specific evidence-based advice on assessment and management CVD and Diabetes using web-based technology simultaneously generate population evidence for evaluating health needs, assessing health care improving the accuracy of risk prediction to feedback into routine practice

provide clinicans with

moment of care,

patient-specific evidence-based advice on assessment and management CVD and Diabetes

using web-based technology

simultaneously generate population evidence for

evaluating health needs,

assessing health care

improving the accuracy of risk prediction

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 Enrolled pop ~660,000, ~500 GPs, 450 practice nurses integrated with MedTech, Next Gen educational seminars to doctors voluntary participation target population according to national guidelines incentive payment $900/GP after 90 patients practice IT facilitators installed software, integration mapping, safe connectivity, one-off training

Enrolled pop ~660,000, ~500 GPs, 450 practice nurses

integrated with MedTech, Next Gen

educational seminars to doctors

voluntary participation

target population according to national guidelines

incentive payment $900/GP after 90 patients

practice IT facilitators installed software, integration mapping, safe connectivity, one-off training

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 who adopted PREDICT? if they adopted it – how did they use it? any significant differences between user groups? what are the most common barriers & challenges to using PREDICT? what factors enable and support practices to start or use PREDICT more? popular images of power users and non-users – true or myths?

who adopted PREDICT?

if they adopted it – how did they use it?

any significant differences between user groups?

what are the most common barriers & challenges to using PREDICT?

what factors enable and support practices to start or use PREDICT more?

popular images of power users and non-users – true or myths?

PREDICT Health Provider Study Aim: describe the patterns of use, barriers, challenges and enablers to the use of PREDICT by primary healthcare providers 3-part qualitative/quantitative evaluation key informant interviews ProCare staff & focus groups (GPs & nurses) Questionnaire GPs/nurses at CME/CNE group meetings (May 2007) descriptive quantitative data analysis Adopters and non-adopters patterns of use over time, characteristics of users

Aim: describe the patterns of use, barriers, challenges and enablers to the use of PREDICT by primary healthcare providers

3-part qualitative/quantitative evaluation

key informant interviews ProCare staff & focus groups (GPs & nurses)

Questionnaire GPs/nurses at CME/CNE group meetings (May 2007)

descriptive quantitative data analysis

Adopters and non-adopters

patterns of use over time,

characteristics of users

Methods substudy-patterns of adoption and use Data collection ProCare Clinical registry+NZ Medical Council data Personal and practice names removed, NZMC/NZNC number retained PREDICT usage data Time and date of PREDICT usage NZMC or NZNC of user Data Linkage- Registry + PREDICT usage data Ethical Approval March 2007 Northern Region Ethics Committee

Data collection

ProCare Clinical registry+NZ Medical Council data

Personal and practice names removed,

NZMC/NZNC number retained

PREDICT usage data

Time and date of PREDICT usage

NZMC or NZNC of user

Data Linkage- Registry + PREDICT usage data

Ethical Approval March 2007 Northern Region Ethics Committee

Results 1: adoption 533 clinicians chose to adopt PREDICT programme doctors (416) and nurses(117) 607 clinicians did not adopt doctors (289) and nurses (318) 75% who did not adopt had compatible patient management systems No differences in adoption by gender,country of medical degree More likely to have vocational registration, and be less than 20yrs since graduation incomplete practice data on 20% doctors and over 25% nurses (funding, location, size of practice)

533 clinicians chose to adopt PREDICT programme

doctors (416) and nurses(117)

607 clinicians did not adopt

doctors (289) and nurses (318)

75% who did not adopt had compatible patient management systems

No differences in adoption by gender,country of medical degree

More likely to have vocational registration, and be less than 20yrs since graduation

incomplete practice data on 20% doctors and over 25% nurses (funding, location, size of practice)

Results: adoption

Results: adoption 0 0

Results 2: patterns of use over time Aug 2002-Jan 2007 45,437 CVD risk assessments were conducted on 25, 705 patients 416 GPs and 117 nurses Used multiple times within one consultation Used subsequent follow-up

Aug 2002-Jan 2007

45,437 CVD risk assessments were conducted on 25, 705 patients

416 GPs and 117 nurses

Used multiple times within one consultation

Used subsequent follow-up

Results : patterns of use New guidelines published PREDICT CVD-Diabetes released ProCare implementation updated PREDICT

Results 3: frequency of use (n= 416 GPs) 31% (n=129)non-user: less than 5 patients 23% (n=95) infrequent 5-20 patients 25% (n=104) frequent user 21-89 patients 21% ( n=88) most frequent (90+ patients)

31% (n=129)non-user: less than 5 patients

23% (n=95) infrequent 5-20 patients

25% (n=104) frequent user 21-89 patients

21% ( n=88) most frequent (90+ patients)

Results: frequency of use (GPs) non-user: less than 5 patients infrequent 5-20 patients compared to frequent user 21-89 patients most frequent user (90+ patients)

non-user: less than 5 patients

infrequent 5-20 patients

compared to

frequent user 21-89 patients

most frequent user (90+ patients)

Results: frequency of use (GPs) Infrequent/non-users- more likely less than 10years since graduation No difference by gender, country of medical degree Older docs (graduated 30yrs+) were as likely to be in either group Frequent/most frequent-more likely to have vocational registration Unable to adjust for FTE

Infrequent/non-users- more likely less than 10years since graduation

No difference by gender, country of medical degree

Older docs (graduated 30yrs+) were as likely to be in either group

Frequent/most frequent-more likely to have vocational registration

Unable to adjust for FTE

Proportion of CVD risk assessments conducted – graphed overtime classified into 4 types emerged Build up then decline Start high then decline Constant All then nothing Results: patterns of use of most frequent GPs (88 GPs)

Proportion of CVD risk assessments conducted – graphed overtime

classified into 4 types emerged

Build up then decline

Start high then decline

Constant

All then nothing

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 Lifecycle of software from first implementation to obselescence 45,000+ risk assessments, ~26,000 patients 92% usage by doctors Large variation in frequency of use No differences by gender or country of medical degree Differences found by having vocation registration and year of graduation may relect time spent working in practice Data limitations

Lifecycle of software from first implementation to obselescence

45,000+ risk assessments, ~26,000 patients

92% usage by doctors

Large variation in frequency of use

No differences by gender or country of medical degree

Differences found by having vocation registration and year of graduation may relect time spent working in practice

Data limitations

This Study Financial incentive ?faciltated uptake, little impact on usage Payment in retrospect, no feedback on progress to target Adoption responsive to annual promotional activites via cell groups Distinct patterns of use may inform interventions for uptake and to maintain sustainable use

Financial incentive ?faciltated uptake, little impact on usage

Payment in retrospect, no feedback on progress to target

Adoption responsive to annual promotional activites via cell groups

Distinct patterns of use may inform interventions for uptake and to maintain sustainable use

Conclusions PREDICT CVD -1st of its kind in NZ This study -some insights into adoption & usage of decision support technology in general practice qualitative analyses and questionnaire results more in-depth understanding for future interventions to improve uptake Learnings/feedback from PREDICT-CVD applied to new module & implementation package Currently 1000+ risk assessments/month with CVD-Diabetes

PREDICT CVD -1st of its kind in NZ

This study -some insights into adoption & usage of decision support technology in general practice

qualitative analyses and questionnaire results more in-depth understanding for future interventions to improve uptake

Learnings/feedback from PREDICT-CVD applied to new module & implementation package

Currently 1000+ risk assessments/month with CVD-Diabetes