1. Decision-Making Research in Supportive Oncology William Pirl, MD, MPH, FAPM, FAPOS Center for Psychiatric Oncology and Behavioral Sciences

2. What is decision-making research?

4. Communication Decision Oncologist gives information on prognosis, options, risks and benefits Oncologist gives information on prognosis, options, risks and benefits Patient preferences Patient preferences Typical Expected Value Decision Model Family member(s) preferences Family member(s) preferences Equation: Value (Y) * p(Y)

5. Assumes decisions are rational

6. Why do rational models fail?  Cognitive biases   Heuristics   Influence of emotions

7. Why do rational models fail?  Cognitive biases  Anchoring   Heuristics   Influence of emotions

8. Why do rational models fail?  Cognitive biases  Anchoring  Framing   Heuristics   Influence of emotions

9. Why do rational models fail?  Cognitive biases  Anchoring  Framing  IKEA effect  Heuristics   Influence of emotions

10. Why do rational models fail?  Cognitive biases  Anchoring  Framing  IKEA effect  Rhyming as reason  Heuristics   Influence of emotions

11. Why do rational models fail?  Cognitive biases  Anchoring  Framing  IKEA effect  Rhyming as reason  Heuristics  Billing by time  Influence of emotions

12. Why do rational models fail?  Cognitive biases  Anchoring  Framing  IKEA effect  Rhyming as reason  Heuristics  Billing by time  Influence of emotions  Anger increases risk taking and quicker judgments

13. Challenges in Research on Medical Decisions  Identifying who is making the decision  Sequence of decisions  Context of decisions and comparability  Speed of decision making  Not always consciously aware of decision

14. Types of studies  Naturalistic  Experimental

15. Naturalistic  Real life decisions  More qualitative  Protocol analysis

16. Processes of Chemotherapy Discontinuation  Chart review of 150 patients in trial of early palliative care  Identified time of last chemotherapy infusion  Qualitatively analyzed what happened before and after  Quantitatively examined relationships with predictors and outcomes

17. Processes Identified ProcessDescriptionEMR Excerpt Definitive decision Final chemotherapy was followed by a documented discussion about permanently stopping chemotherapy “Worsening performance status with pressure ulcer, falls at home, and generalized weakness. We discussed goals of care and the concern that more chemo could hurt him rather than help him at this point given his declining performance status. He is very much on board with focusing on symptom control and we will initiate hospice services at home.” Deferred decision (break) Documentation of a discussion about explicitly discontinuing chemotherapy with plans to re-evaluate and consider further treatment “We discussed whether to continue chemotherapy for 40 minutes. Her disease remains stable after an initial response, but she is having fatigue, anemia, and also has this dizziness which could be from chemo. We agreed that it makes sense to go on a holiday from chemotherapy. We can restage in 2 months and, if stable, hopefully continue the chemo-break through the holiday season. If her dizziness resolves and her disease grows again in the future, we could consider (specific chemotherapy regimen).” Disruption from radiation treatment Chemotherapy held for the initiation of radiation for brain or bone metastases and hemoptysis and documentation of intent for potential chemotherapy treatment after completing radiation “Given that she will be starting radiation, we will hold chemo today. Will return after radiation for consideration of further chemotherapy.” Disruption from hospitalization Patient hospitalized before next scheduled infusion and chemotherapy was never restarted “Continued therapy directed at her cancer, which was the principal challenge for her, now appeared to offer her very little in terms of quality or quantity of life and with some reluctance her devoted husband agreed to a do not resuscitate/do not intubate (DNR/DNI) status and comfort measures only.” No decisionPatient died before receiving scheduled chemotherapy and there was no documentation of stopping chemotherapy “She is rarely leaving the house, is not letting family help, and is perseverating about her funeral…. She is doing well physically. Labs are adequate for therapy today and will start chemotherapy today.” The patient received chemotherapy that day and then died six days later.

18. Predictors of Processes Predictors Break n=18 (22.2%) Definitive Discussion n=16 (19.7%) Disruption from XRT n=18 (22.2%) Disruption from hospitalization n=22 (27.2%) Not discontinued n=7 (8.6%) p-value Mean age (sd)67.6 (8.9)65.5 (10.6)62.6 (7.6)61.0 (10.2)65.3 (10.0)0.09 Female9 (50.0%)6 (37.5%)11 (61.1%)7 (31.8%)4 (57.1%)0.26 Married9 (50.0%)12 (75.0%)15 (83.3%)11 (50.0%)3 (42.9%)0.09 White race18 (100%)16 (100%)18 (100%)20 (90.9%)7 (100%)0.24 Baseline ECOG 0 1 2 7 (38.9%) 8 (44.4%) 3 (16.7%) 5 (31.3%) 11 (68.8%) 0 (0%) 7 (38.9%) 4 (22.2%) 8 (36.4%) 14 (63.6%) 0 (0%) 3 (42.9%) 1 (14.3%) 0.26 Median lines of chemotherapy 1211.520.30 Randomization Standard Care Early Palliative Care 5 (11.1%) 13 (36.1%) 9 (20.0%) 7 (19.4%) 11 (24.4%) 7 (19.4%) 17 (37.8%) 5 (13.8%) 3 (6.7%) 4 (11.1%) 0.03

19. Outcomes Break Definitive Discussion Disruption from XRT Disruption from hospitalizati on Not discontinued p-value Median days before death (range) 168.5 (30- 548) 50 (13-287)67 (35-250)31 (13-133)6 (3-17)<0.001 Chemotherapy within 14 days of death 0 (0%)1 (6.3%)0 (0%)2 (9.1%)5 (71.4%)<0.001 Hospice15 (83.3%)16 (100%)13 (72.2%)12 (54.6%)0 (0%)<0.001 Median days in Hospice (range) 8.5 (0-140)29.5 (1-268)7 (0-66)1 (0-116)00.001 Death in Hospital 3 (16.6%)0 (0%)2 (11.1)12 (54.6%)2 (28.6%)0.001 Eventual final decision 13 (72.2%)16 (100%)11 (61.1%)20 (90.9%)0 (0%)<0.001

20. Findings  93% of time oncologist documented giving patient option of stopping, patient chose stopping  Heuristic based on harm  Stopping chemotherapy and referring to hospice are two distinct decisions

21. Major Limitation  Oncologists’ documentation

22. Major Limitation  Oncologists’ documentation  Think out loud technique

23. Major Limitation  Oncologists’ documentation  Think out loud technique  Survey oncologists’ after encounters

24. Major Limitation  Oncologists’ documentation  Think out loud technique  Survey oncologists’ after encounters  Audiotape encounter

25. Major Limitation  Oncologists’ documentation  Think out loud technique  Survey oncologists’ after encounters  Audiotape encounter  Videotape encounter

26. Major Limitation  Oncologists’ documentation  Think out loud technique  Survey oncologists’ after encounters  Audiotape encounter  Videotape encounter Challenge is when do these decisions occur?

27. Experimental  Some control of context  Causal inferences

28. Experimental Designs  Randomized standardized encounters with manipulations  Vignettes  Standardized patients  Computerized patient simulations  Randomized controlled trial of intervention

29. Examples of Randomized Standardized Situation with Manipulations  Case of 74 year old white patient on dialysis presents with newly diagnosed metastatic small cell lung cancer

30. Examples of Randomized Standardized Situation with Manipulations  Case of 41 year old white patient on dialysis presents with newly diagnosed metastatic small cell lung cancer

31. Examples of Randomized Standardized Situation with Manipulations  Case of 41 year old black patient on dialysis presents with newly diagnosed metastatic small cell lung cancer

32. Examples of Randomized Standardized Situation with Manipulations  Case of 41 year old black patient on dialysis presents with newly diagnosed metastatic breast cancer

33. Examples of Randomized Standardized Situation with Manipulations  Case of 41 year old black patient presents with newly diagnosed metastatic breast cancer

34. Examples of Randomized Standardized Situation with Manipulations  Case of 41 year old black patient presents with newly diagnosed metastatic breast cancer Half sample does mindfulness meditation before viewing the case

35. Limitations  Limited by how much information given and realism  Not real life

36. Examples of Randomized Controlled Trials of an Intervention  Trial of intervention itself: Use of defaults in asking about code status  Trial testing the influence of a factor on decisions (interaction): influence of oncologists’ dispositional affect on EOL discussions in a trial of early palliative care

37. Oncologist Patient Randomization Early Palliative Care Integrated with Standard Care Rates of End-of-Life Discussions Patient Standard Care Rates of End-of-Life Discussions

38. Oncologist Patient Randomization Early Palliative Care Integrated with Standard Care Rates of End-of-Life Discussions Patient Standard Care Rates of End-of-Life Discussions Assess disposition al negative affect

39. Feelings Measured NegativeDistressed Irritable Upset Ashamed Guilty Nervous Scared Jittery Hostile Afraid PositiveInterested Alert Excited Inspired Strong Determined Enthusiastic Attentive Proud Active Positive and Negative Affect Schedule (PANAS )

40. Oncologist Patient Randomization Early Palliative Care Integrated with Standard Care Rates of End-of-Life Discussions Patient Standard Care Rates of End-of-Life Discussions Assess disposition al negative affect

41. Oncologist Patient Randomization Early Palliative Care Integrated with Standard Care Rates of End-of-Life Discussions Patient Standard Care Rates of End-of-Life Discussions Are patients with oncologists who have higher negative affect more likely to report an EOL discussion if they were in the early palliative care arm? Assess disposition al negative affect

42. Limitations  Risky but based on theory  Intervention might be having other effects  Many smart people don’t understand interactions

43. Decision-Making Studies  Require more thought in planning  More abstract  Decades of research outside of medicine  Low-intensity interventions have potential for large scale impact