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

Approaches to decision making

How do we make decisions?

• Based on facts and figures: Apollonian rationality?

• Gut instincts: Dionysian feeling?

• Both?

One model of decision making: pure rationality

New hot dog stand location?

New hot dog stand location?

Rush StRush St

LaSalle StLaSalle St

20%20%

80%80%

90%90%

10%10%

$500$500

$200$200

$450$450

$900$900

“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

Is this a descriptive theory of human

decision making?• NO

• ‘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

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

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

Medical decision making

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

Robotic prostatectomy

External beam radiotherapy

BrachytherapyActive surveillance

If I choose surgery, I may leak urine…if I choose surveillance, I may worry about cancer spreading

surgery

radiation

‘experimental’ options (cryotherapy, primary hormonal therapy, etc)

Decision choice for a man with moderate risk localized prostate cancer

Open radical prostatectomy

“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

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

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

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

But…..

• This takes a long time

• Not compensated

• Not all patients prefer this mode of decision making/feel comfortable with numbers/ science

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

Challenges addressed by shared decision making tools

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

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

Decision analysis for prostate cancer

Urinary incontinence 5%

Erectile dysfunction 50%

Cancer death 15%

Erectile dysfunction 20%

Urinary incontinence 3%

Cancer death 30%

Erectile dysfunction 10%

Urinary incontinence 1%

Cancer death 35%

surgery

radiation

Active surveillance

Decision analysis for prostate cancer

Urinary incontinence 5%

Erectile dysfunction 50%

Cancer recurrence 15%

Erectile dysfunction 20%

Urinary incontinence 3%

Cancer recurrence 30%

Erectile dysfunction 10%

Urinary incontinence 1%

Cancer death 35%

surgery

radiation

Active surveillance

Value:40

Value:80

Value: 5

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

surgery?

Patient preference assessment

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

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)

How do you measure utility?

Traditional ways to quantify preferences:

• Standard Gamble

• Time Trade Off

• Rating Scale

Consumer preference measurement: conjoint analysis

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

Developing a conjoint application

• “Voice of the customer” approach

• Relevance for other patient/stakeholder engagement efforts?

60-90 min.Interviews:treatments,Side effects,outcomes

60-90 min.Interviews:treatments,Side effects,outcomes

Side effects

Outcomes

1,000 quotes

Side effects

Outcomes

1,000 quotes

ResearchResearchTeam Team

IdentifiesIdentifies1515

ThemesThemes

ResearchResearchTeam Team

IdentifiesIdentifies1515

ThemesThemes

ResearchersResearchersNarrowNarrow

From 1,000From 1,000to 70 to 70 quotesquotes

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From 1,000From 1,000to 70 to 70 quotesquotes

PatientsGroupSimilarQuotes

into piles

PatientsGroupSimilarQuotes

into piles

ResearchersAnalyze piles

Using AHCfor consensus

groupings

ResearchersAnalyze piles

Using AHCfor consensus

groupings

TeamIdentifiesConjoint

AttributesFrom piles

TeamIdentifiesConjoint

AttributesFrom piles

ListenListen ParseParse ThemesThemes SelectSelect AffinityAffinity AnalyzeAnalyze TranslateTranslate

Methods

“Voice of the Patient” Process

Objective Subjective More Subjective

Methods

SexSex: If you have an understanding partner, the ED thing can be ok.

UrinaryUrinary: Changing pads frequently…feels as if you don't have control of your life.

BowelBowel: The bowel issue is the biggest deal because it is socially unacceptable.

LifespanLifespan: It is more important to stay alive, regardless of the side effects.

Others' AdviceOthers' Advice: I only follow doctors’ advice up to a point. Not 100%

ActionAction: I was just thinking "we have got to do something"

CuttingCutting: I don't want to be cut; I don't want to have surgery.

CautionCaution: I could wait for a while if the numbers stay stable…

Treatment Issues Side Effects

ListenListen ParseParse ThemesThemes SelectSelect AffinityAffinity AnalyzeAnalyze TranslateTranslate

Sample narratives from men treated for prostate cancer

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)

Methods

(7) Seven Patient-derived attributes:

1. Sexual function2. Urinary function3. Bowel function4. Survival5. “Active/Cautious”6. Requirement for incision7. Opinion of significant others

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

ResultsCharacteristic Mean (% of n=31) Characteristic Mean (% of n=31)

Age 64 ± 4, range 55 to 73 Current smokerRace/ethnicity Yes 5 (16%) White (non-Hispanic) 10 (32%) No 26 (84%) Black/African American 13 (42%) Medical conditions Hispanic/Latino 5 (16%) Diabetes 7 (23%) Other or mixed race/ethnicity 3 (10%) Heart attack 6 (19%)Partnership status Stroke 0 (0%) Living with spouse or partner 19 (61%) Amputation 1 (3%) Signif. relationship, not living together 2 (6%) Circulation problems 7 (23%) Not in a significant relationship 10 (32%) Asthma, emphysema, breathing probs. 4 (13%)Marital status Stomach ulcer or irritable bowel 3 (10%) Currently married 14 (45%) Kidney disease 1 (3%) Not currently married 17 (55%) Major depression 4 (13%)Employment status Seizures 0 (0%) Employed 10 (32%) Alcoholism or alcohol problems 5 (16%) Not employed 9 (29%) Drug problems 4 (13%) Retired 12 (39%) Control preferences scaleEducational attainment Mostly doctor making decision 3 (10%) High school graduate or less 4 (13%) Doctor and self together 15 (48%) Some college 17 (55%) Mostly self 13 (42%) College graduate 10 (32%) Problems in last 4 weeksHousehold income Urinary function 11 (35%) Less than $10,000 5 (17%) Bowel habits 2 (6%) $10,000 to $30,000 13 (43%) Sexual function 11 (35%) More than $30,000 12 (40%) Hot flashes 0 (0%)

Breast tenderness/enlargement 0 (0%) Depressed 0 (0%) Lack of energy 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)

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

Results: Internal validity(R2 = % of variance in 16 stimuli scores

explained by utility functions)M

ean

R2 88% 87%

55%50%

60%

70%

80%

90%

Conjoint Ratings TimeTradeoff

P>.05

P=.001

P-values are from paired comparisons (t-tests) with conjoint analysis.

Results: Predictive validity for 3 methods

(hit rate:1st choice out of 4 options)

68%

56%

47%

68%63%

47%

25%

35%

45%

55%

65%

Conjoint Ratings Time Tradeoff

Hit

Rat

e: 1

of 4

1st Choice Hit Rate -Conjoint Stimuli

1st Choice Hit Rate -Holdout Stimuli

P>.05

P>.05

P>.05 P>.05

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 analysis

Time tradeoff Rating scaleConjoint vs. time

tradeoffConjoint vs. rating scale

(N = 31) (N = 15) (N = 16) (N = 15) (N = 16)Ease of use Very easy/easy/ somewhat easy 18 (58%) 10 (67%) 14 (88%) Somewhat/very difficult 13 (42%) 5 (33%) 2 (12%)Satisfaction Extremely/somewhat 26 (84%) 9 (60%) 13 (81%)

Neutral/not very/not at all 5 (16%) 6 (40%) 3 (19%)

P-values obtained by comparing responses within same subjects using the exact version of McNemar’s test of paired proportions.

P = .38 P = .99

P = .99 P = .03

Rating Scale perceived to be easier than Conjoint…but Conjoint’s satisfaction ratings are just as good

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

Additive value of conjoint analysis-based preference assessment over

tradictional SDM aid

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.

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

Results

Decisional Conflict

Satisfaction with Care

Results: Prostate Cancer Knowledge

60

62

64

66

68

70

72

74

76

78

80

Intervention Control

Conclusions

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

Next frontiers

• 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)