Improving decision making at the point of care: opportunities and challenges

1. Christopher Saigal MD MPH Associate Professor Department of Urology Geffen School of Medicine at UCLA Improving decision making at the point of care: opportunities and challenges

2. Approaches to decision making

3. How do we make decisions? Based on facts and figures: Apollonian rationality? Gut instincts: Dionysian feeling? Both?

4. One model of decision making: pure rationality $900 20% Rush St New hot dog stand location? $450 80% $500 90% LaSalle St 10% $200

5. “Expected value” La Salle Street safe strategy: (.9 x $500) + (.1 x $200)= $470/week Rush Street risky strategy: (0.2 x $900) + (0.8 x $450) = $540/week The rational decider goes for the Rush Street location

6. Is this a descriptive theory of human decision making? NO ‘behavioral economics’ Framing biases/loss aversion Bubbles and panics

7. Intuitive decision making can be key • Many decisions are best executed in response to gut feelings (“blink”) • See a prairie fire coming towards you: run to the river • Without the orbitofrontal cortex, decision making becomes impossible

8. Rational decision making can be key Some decisions are best made with a rational framework Which credit card: - intro teaser rate of 2.9% for 1 year, then goes to 16% - intro rate of 4.9% that goes to 12% at one year

9. Best model: useful combination of both styles of decision making Humans function decide best when knowing which method to rely on- or when to combine

10. Medical decision making

11. The double-edged sword Constant innovation in treatments for patients Treatments can offer trade-offs Decisions have multiple moving parts Patient preferences and values are key deciding factors in many situations

12. Decision choice for a man with moderate risk localized prostate cancer Robotic prostatectomy surgery If I choose surgery, I may leak urine…if I choose surveillance, I may worry about cancer spreading Open radical prostatectomy ‘experimental’ options (cryotherapy, primary hormonal therapy, etc) radiation External beam radiotherapy Active surveillance Brachytherapy

13. “Bounded rationality” Complex decision Time constraints Limits on human computational ability “ A wealth of information creates a poverty of attention” Can software expand these “bounds?” Simon, Am Economic Review, 1978

14. What is the ideal decision in healthcare? Patient-centered decision A patient-centered decision is one which reflects the needs, values and expressed preferences of a well-informed patient Sepucha, Health Affairs 2004

15. Defining decision quality A high quality patient decision is one in which the patient has: • Leveraged a useful level of decision specific knowledge • Expressed his values for the outcomes of interest for the decision at hand • Achieved congruence between values and ultimate treatment choice Sepucha 2004

16. Achieving the ideal decision: Shared Decision Making Many definitions Shared decision making is the collaboration between patients and physicians to come to an agreement about a healthcare decision It is especially useful when there is no clear "best" treatment option

17. But….. This takes a long time Not compensated Not all patients prefer this mode of decision making/feel comfortable with numbers/ science

18. Potential solution: decision aids Many formats Can take advantage of IT to personalize information, use video, interactivity Save time, can be used at home, in waiting rooms, etc

19. Challenges addressed by shared decision making tools

20. Decision Aids Increase patient involvement Increase patient knowledge Clarify values, increase concordance between values and choices Reduce decisional conflict, regret (? Lawsuits O’Connor Cochrane Collaboration 2006

21. Next generation approach: personalized decision analysis “rational model” Accounts for all possible outcomes Accounts for the probabilities of the outcomes ‘Weighs’ the desirability of the outcomes

22. Decision analysis for prostate cancer Erectile dysfunction 50% Urinary incontinence 5% surgery Cancer death 15% radiation Erectile dysfunction 20% Urinary incontinence 3% Cancer death 30% Active surveillance Erectile dysfunction 10% Urinary incontinence 1% Cancer death 35%

23. Decision analysis for prostate cancer Value:40 Erectile dysfunction 50% Urinary incontinence 5% Value:80 surgery Cancer recurrence 15% Value: 5 radiation Erectile dysfunction 20% Urinary incontinence 3% Cancer recurrence 30% Active surveillance Erectile dysfunction 10% Urinary incontinence 1% Cancer death 35%

24. How can we measure the strength of your desire to avoid diapers after surgery?

25. Patient preference assessment

26. What is a ‘utility’value? Derived from classical economics A health ‘utility’ is a number, ranging from 0.0 to 1.0, which corresponds to a person’s desire for a health state Determined under a conditions of uncertainty Expected utility theory is a ‘normative’ description Von Neumann and Morgenstern 1944

27. Ways in which we can use patient preferences 1 year in health state with a utility of 0.85 = 0.85 quality adjusted life years (QALY)

28. How do you measure utility? Traditional ways to quantify preferences: Standard Gamble Time Trade Off Rating Scale

29. Consumer preference measurement: conjoint analysis

30. Conjoint analysis Can more easily incorporate non-clinical treatment attributes of importance to patients More accurate assessments of preferences may lead to treatment choices more congruent with patients’ goals More intuitive- leverages emotional intelligence

31. Developing a conjoint application • “Voice of the customer” approach • Relevance for other patient/stakeholder engagement efforts?

32. Methods “Voice of the Patient” Process 60-90 min. Interviews: treatments, Side effects, outcomes Side effects Outcomes 1,000 quotes Research Team Identifies 15 Themes Researchers Narrow From 1,000 to 70 quotes Patients Group Similar Quotes into piles Researchers Analyze piles Using AHC for consensus groupings Team Identifies Conjoint Attributes From piles Listen Parse Themes Select Affinity Analyze Translate Objective Subjective More Subjective

33. Methods Sample narratives from men treated for prostate cancer Treatment Issues Side Effects Cutting: I don't want to be cut; I don't want to have surgery. Sex: If you have an understanding partner, the ED thing can be ok. Others' Advice: I only follow doctors’ advice up to a point. Not 100% Urinary: Changing pads frequently…feels as if you don't have control of your life. Caution: I could wait for a while if the numbers stay stable… Lifespan: It is more important to stay alive, regardless of the side effects. Action: I was just thinking "we have got to do something" Bowel: The bowel issue is the biggest deal because it is socially unacceptable. Listen Parse Themes Select Affinity Analyze Translate

34. Methods • Randomized trial of conjoint analysis versus time trade off and rating scale methods • “Voice of the customer” adaptation to identify attributes of importance to patients • Development of rating scale and time trade off applications • Development of novel form of real-time conjoint analysis: Adaptive Best-worst Conjoint (ABC)

35. Methods (7) Seven Patient-derived attributes: • Sexual function • Urinary function • Bowel function • Survival • “Active/Cautious” • Requirement for incision • Opinion of significant others

36. Methods • Recruited men at the VA urology clinic undergoing prostate needle biopsy for suspicion of prostate cancer • Eligible men: Negative biopsy, able to read English • Subjects and task order randomized to: Rating Scale vs. Adaptive Best-worst Conjoint Time Tradeoff vs. Adaptive Best-worst Conjoint

37. Results

38. Results Outcome metrics: -Compared internal validity of methods -Comparative ability of stated preference data to predict preferences for health states that were not explicitly rated by patient -Compared patient acceptability in men being evaluated for prostate cancer

39. Results: Internal validity(R2 = % of variance in 16 stimuli scoresexplained by utility functions) P>.05 Mean R2 P=.001 P-values are from paired comparisons (t-tests) with conjoint analysis.

40. Results: Predictive validity for 3 methods (hit rate:1st choice out of 4 options) P>.05 P>.05 P>.05 P>.05 P-values are from paired comparisons (McNemar tests) with conjoint analysis.

41. Results: Patient satisfaction and Ease-of-Use scores Rating Scale perceived to be easier than Conjoint…but Conjoint’s satisfaction ratings are just as good

42. Conclusions • Conjoint analysis is a feasible method to collect real-time, individual level preferences from patients • Conjoint analysis is viewed by patients as a satisfactory way to collect preference data, though challenging

43. Additive value of conjoint analysis-based preference assessment over tradictional SDM aid

44. Methods Men randomized to education and preference assessment receive a report detailing their preferences Counseling physicians briefed on report interpretation Physicians could use the report during the counseling session.

46. Methods Decision quality measures (pre/post): • Satisfaction with care • Disease specific knowledge • Decisional Conflict Scale • Shared decision making questionnaire • Yes/No has made a treatment choice

47. Results

48. Decisional Conflict

50. Satisfaction with Care