CVDecide ImplementationCVDecide ImplementationJanuary 2013January 2013

Richard Thomson Professor of Epidemiology and Public Health

Decision Making and Organisation of Care Research ProgrammeInstitute of Health and Society

Newcastle upon Tyne Medical School

UK Policy: UK Government

Shared decision making will become the norm:

“No decision about me without me”

MAGICmaking good decisions

Thanks for the decision aid…

I prefer this option Doctor

Models of clinical decision making in the

consultation

Paternalistic Informed ChoiceShared Decision Making

Patient well informed (Knowledge)

Knows what’s important to them (Values elicited)

Decision consistent with values

SDM is an approach where clinicians and patients make decisions together using the best available evidence. (Elwyn et al. BMJ 2010)

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

“Shall I have a knee

replacement?”

“Shall I have a prostate

operation?”

“Shall I take a statin tablet for the rest of my

life?”

“Should I use insulin or an alternative?”

“I would like to lose weight”

“I would like to eat/smoke/drink

less”

Spectrum of SDM to SMS

TO

OL

S

SK

ILL

S

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

Are patients involved?Patients who would like more invelvement in decisions about

their care (source: NHS Inpatient Surveys 2002 - 2011)

45 46 47 47 48 49 48 48 48 48

0

10

20

30

40

50

60

70

80

90

100

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Year

Per

cen

tag

e

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

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

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.

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

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

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)

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)

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”

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

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

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