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

• Based on facts and figures: Apollonian rationality?
• Gut instincts: Dionysian feeling?
• Both?

One model of decision making: pure rationality
$900
Rush St
New hot dog stand location?
$450
$500
LaSalle St
$200

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

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

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

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

• Humans function decide best when knowing which method to rely on- or when to combine

• 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

Decision choice for a man with moderate risk localized prostate cancer surgery
Robotic prostatectomy
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)
Active surveillance radiation External beam radiotherapy
Brachytherapy

• 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

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

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

• 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

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

• Many formats
• Can take advantage of IT to personalize information, use video, interactivity
• Save time, can be used at home, in waiting rooms, etc

Challenges addressed by shared decision making tools

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

• “rational model”
• Accounts for all possible outcomes
• Accounts for the probabilities of the outcomes
• ‘Weighs’ the desirability of the outcomes

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

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

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

• 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

1 year in health state with a utility of 0.85
=
0.85 quality adjusted life years
(QALY)

Traditional ways to quantify preferences:
• Standard Gamble
• Time Trade Off
• Rating Scale

Consumer preference measurement: conjoint analysis
Touch screen Keyboard
2 month wait
4G network
No wait
3G network

• 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

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

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

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

• 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 )

(7) Seven Patient-derived attributes:
1.
Sexual function
2.
Urinary function
3.
Bowel function
4.
Survival
5.
“Active/Cautious”
6.
Requirement for incision
7.
Opinion of significant others

• 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

Characteristic
Age
Race/ethnicity
White (non-Hispanic)
Black/African American
Hispanic/Latino
Other or mixed race/ethnicity
Partnership status
Living with spouse or partner
Signif. relationship, not living together
Not in a significant relationship
Marital status
Currently married
Not currently married
Employment status
Employed
Not employed
Retired
Educational attainment
High school graduate or less
Some college
College graduate
Household income
Less than $10,000
$10,000 to $30,000
More than $30,000
Results
Mean (% of n=31)
64 ± 4, range 55 to 73
10 (32%)
13 (42%)
5 (16%)
3 (10%)
19 (61%)
2 (6%)
10 (32%)
14 (45%)
17 (55%)
10 (32%)
9 (29%)
12 (39%)
4 (13%)
17 (55%)
10 (32%)
5 (17%)
13 (43%)
12 (40%)
Characteristic
Current smoker
Yes
No
Medical conditions
Diabetes
Heart attack
Stroke
Amputation
Circulation problems
Asthma, emphysema, breathing probs.
Stomach ulcer or irritable bowel
Kidney disease
Major depression
Seizures
Alcoholism or alcohol problems
Drug problems
Control preferences scale
Mostly doctor making decision
Doctor and self together
Mostly self
Mean (% of n=31)
5 (16%)
26 (84%)
7 (23%)
6 (19%)
0 (0%)
1 (3%)
7 (23%)
4 (13%)
3 (10%)
1 (3%)
4 (13%)
0 (0%)
5 (16%)
4 (13%)
3 (10%)
15 (48%)
13 (42%)
Problems in last 4 weeks
Urinary function
Bowel habits
Sexual function
Hot flashes
Breast tenderness/enlargement
Depressed
Lack of energy
11 (35%)
2 (6%)
11 (35%)
0 (0%)
0 (0%)
0 (0%)
1 (3%)
Change in body weight 1 (3%)
Functioning problems were dichotomized as no (no problem or very small problem) or yes (small, moderate or large problem)

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

Results: Internal validity
( R 2 = % of variance in 16 stimuli scores explained by utility functions )
90%
80%
70%
60%
50%
88%
P>.05
87%
P=.001
55%
Conjoint Ratings Time
Tradeoff
P-values are from paired comparisons (t-tests) with conjoint analysis.

Results: Predictive validity for 3 methods
( hit rate :1 st choice out of 4 options )
65%
55%
68% 68%
P>.05
P>.05
63%
56%
45%
35%
P>.05
P>.05
47% 47%
1st Choice Hit Rate -
Conjoint Stimuli
1st Choice Hit Rate -
Holdout Stimuli
25%
Conjoint Ratings Time Tradeoff
P-values are from paired comparisons (McNemar tests) with conjoint analysis.

Results: Patient satisfaction and
Ease-of-Use scores
Preference assessment method ease of use and satisfaction (categories collapsed)
Conjoint
Time tradeoff Rating scale
Conjoint vs. time analysis tradeoff
(N = 31) (N = 15) (N = 16) (N = 15)
Ease of use
Very easy/easy/ somewhat easy
Somewhat/very difficult
Satisfaction
Extremely/somewhat
18 (58%)
13 (42%)
26 (84%)
10 (67%)
5 (33%)
9 (60%)
14 (88%)
2 (12%)
13 (81%)
P = .99
P = .38
Neutral/not very/not at all 5 (16%) 6 (40%) 3 (19%)
Conjoint vs. rating scale
P-values obtained by comparing responses within same subjects using the exact version of McNemar’s test of paired proportions.
(N = 16)
P = .03
P = .99
Rating Scale perceived to be easier than Conjoint… but Conjoint’s satisfaction ratings are just as good

• 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

Additive value of conjoint analysisbased preference assessment over tradictional SDM aid

• 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.

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

80
78
76
74
72
70
68
66
64
62
60
Results: Prostate Cancer
Knowledge
Intervention Control

Conjoint analysis is a feasible method to collect real-time, individual level preferences from patients in a busy clinic
Pilot data indicate:
-increased patient satisfaction after formal preference assessment, reduced decisional conflict
-perception of physician thoroughness enhanced

• Deployment of integrated decision analysis- preference measurement application at (UCLA)
• Identify barriers to actual shared decision making behaviors in men who have viewed a decision aid and express readiness to engage in shared decision making (PCORI)