CVDecide Implementation January 2013

1. CVDecide Implementation January 2013 Richard Thomson Professor of Epidemiology and Public Health Decision Making and Organisation of Care Research Programme Institute of Health and Society Newcastle upon Tyne Medical School

2. UK Policy: UK Government Shared decision making will become the norm: “No decision about me without me”

3. Thanks for the decision aid… I prefer this option Doctor MAGIC making good decisions

4. Models of clinical decision making in the consultation SDM is an approach where clinicians and patients make decisions together using the best available evidence. (Elwyn et al. BMJ 2010) Shared Decision Making Informed Choice Paternalistic Patient well informed (Knowledge) Knows what’s important to them (Values elicited) Decision consistent with values

5. Examples of preference –sensitive decisions • Breast conserving therapy or mastectomy for early breast cancer • Repeat c-section or trial of labour after previous c-section • Watchful waiting or surgery for benign prostatic hypertrophy • Statins or diet and exercise to reduce CVD risk

6. TOOLS SKILLS Spectrum of SDM to SMS “Shall I have a knee replacement?” “Shall I take a statin tablet for the rest of my life?” “I would like to lose weight” “Shall I have a prostate operation?” “I would like to eat/smoke/drink less” “Should I use insulin or an alternative?”

7. Cochrane Review of Patient Decision Aids(O’Connor et al 2011): Improve knowledge More accurate risk perceptions Feeling better informed and clear about values More active involvement Fewer undecided after PDA More patients achieving decisions that were informed and consistent with their values Reduced rates of: major elective invasive surgery in favour of conservative options; PSA screening; menopausal hormones Improves adherence to medication (Joosten, 2008) Better outcomes in long term care SDM – evidence

8. Are patients involved?

9. Why is decision support needed? • To allow high quality preference-based shared decisions to be made Why? • Give patients the treatment they need and no less, and the treatment they want and no more. (Al Mulley) • No decisions in the face of avoidable ignorance • Reduce un-warrranted variation

10. Aims of CVDecide project • to produce an interactive tool to assist cardiovascular risk communication that will form part of GPs’ existing electronic desktops • to extend the potential of the tool beyond assessment of baseline risk by introducing estimates of risk and benefit of preventive interventions

11. Development • Software development and incorporation within EMIS. • Incorporation of predictive equations • Framingham and QRISK equations for cardiovascular risk prediction • Evidence-based predictive models for the effectiveness of interventions, including lifestyle changes • Adverse effects based on robust data. • A period of iterative development with clinicians and patients to assess acceptability and usability.

12. Implementation pilot • Service based usability testing and process evaluation in a sub-set of practices. • Make CVDecide available for further learning and testing. • Work on requirements for roll out • Link to Health Check programme

13. Results • Three practices, six practitioners, 24 patients • Consultation times 20-30 mins; tool open from 1- 19 mins (mean 4.7) per patient and 1.6 -12 mins by clinician • Patients better prepared to decide after clinic (Deliberate scale) • Mean (SD) change in score for perceived behavioural control was10.7 (4.5). • Greater intention towards beneficial change and greater perceived behavioural control regarding lifestyle factors • Increased accuracy of risk perception

14. Results • All patients would recommend this consultation to a friend • “visual impact (M61) • “it’s explained a lot better than normally”(F73) • “it brings a smile to your face as well”(F43) • “the calculation on the computer told me”(M73), • “opened your mind up”(F50) • “for them [practitioner] to actually flag it up on a screen gives you the ability to discuss what is up there with the other person as well” (F43) • “It highlights the fact it can happen to anybody, your 100 faces”(F53)

15. Results • “It highlights the fact it can happen to anybody, your 100 faces”(F53) • “In my case it was 29% of naughty red faces and if I stopped smoking it would reduce by 12%” (M64). • “I’d rather do that [loose weight] than go on statins” but then acknowledged that if weight loss was not successful he would, “consider the doctor’s opinion of going onto statins.”(M66)

16. Results:HCPs • Reported as quick, clever, visual and that patients seemed to like it • “not going to work [in 10 minute consultation]” • “by doing it themselves, with diet and exercise they could, maybe half their risk for some” • “not persuaded, but quite keen to try [lifestyle changes]” • “I don’t like medications – that phrase comes up very frequently, and the other thing is that people sometimes underestimate what they can do by lifestyle”

17. Improving the tool • Generally very well received • Easy to use after limited practice • Capacity to print out • Write back to record (align with SOP) • Access to QRISK • Consistency of risk communication within the service • Issues of who should use which components (e.g. related to prescribing) • Use in EMIS web

18. Conclusions: Update • Fully developed and integrated tool for use in EMIS • Further amendments have been made • QRISK or Framingham • Writes back to clinical record (including local SOP READ codes) • Print out options • Better recorded action plan

19. Conclusions: Roll out • Three phase roll out, with review at end of each stage • Random selection and offer • Support • Web site (http://www.ncl.ac.uk/ihs/research/dmoc/cvdecide/index.htm ) • Training (includes optional 2-3 hr advanced skills) • Technical (see web site) • Evaluation • Patient and clinician interviews • Log data