Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Decision Fatigue Among Physicians

Han Ye, Junjian Yi, Songfa Zhong

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Questions

• Why Barack Obama in gray or blue suit?

• Why Mark Zuckerberg in gray T-shirt?

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Questions

• Why Barack Obama in gray or blue suit?

• Why Mark Zuckerberg in gray T-shirt?

Obama: “You’ll see I wear only gray or blue suits. I’m trying to paredown decisions. I don’t want to make decisions about what I’m eatingor wearing. Because I have too many other decisions to make.”

Zuckerberg: “I really want to clear my life to make it so that I have tomake as few decisions as possible about anything except how to bestserve this community.”

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Research Questions

• Whether and how decision fatigue affects physician behavior?

• What kind of physicians are more vulnerable to decision fatigue?

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Literature: Decision Fatigue

Making too many decisions depletes individuals’ executive functionand mental resources, which may influence their subsequent decisions(Baumeister et al., 1998; Stanovich and West, 2000; Kahneman, 2011;Baumeister and Tierney, 2012).

• Consumer purchasing for custom-made products (Levav et al.,2010)

• Judicial parole decisions (Danziger et al., 2011)

• Voter behavior (Augenblick et al., 2015)

• Financial analysts’ forecasts (Hirshleifer et al., 2017)

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Literature: Physician Behavior

Physicians’ decisions are affected by a variety of factors irrelevant topatient health, which may lead to large variations in procedure use,medical expenditure and patient outcomes.

• Peer effects among doctors and organizational culture (Lee andMongan, 2009)

• Medical liability system (Currie and McLeod, 2008; Frakes, 2013)

• Physician beliefs about treatment (Cutler et al., 2013)

• Physicians’ financial incentives (Clemens and Gottlieb, 2014)

• Quality of physicians’ human capital (Currie and McLeod, 2017)

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Patient Flow

Source: The hospital A&E website6 / 50

Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Patient Flow Data

An entire set of ED visits from January 2011 to December 2012 in anacute general hospital of SG (264,115 patient cases).

For each patient case, we observe detailed timestamps (e.g. thestart and end times of triage, consultation, and task orders) andphysician identifiers.

With these data, we are able to reconstruct

• Real-time patient flow volume in the ED

• Patient’s entire path through the ED

• Physician’s shift schedules

• Sequences of actual patients who are seen by the physician ineach shift

• Potential patients who arrive during a time when the physician’sshift is in progress

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Patient Information

• Basic demographics: birthdate, gender, address

• Patient acuity category scale (PACS)

• Arrival mode: ambulance or walk in

• Disposition type: discharge home, follow-up in primary care,inpatient admission ...

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Sample Restrictions

All patient cases whose attending physician

• has at least 10 shifts observed

• is working in a shift with shift length 6 -16 hours

• 242,761 patient cases, with 124 physician identities

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Dependent Variables

Physician decisions

1. physician discharge decision (dummies)

• outpatient disposition (discharge home or follow-up)• discharge home• inpatient admission

2. task orders

• total number of task orders (treatments and diagnostic tests)• number of diagnostic tests

3. patient length of stay

• from the start to the end of patient case consultation

Patient outcomes (dummies)

1. death in the ED

2. return visits to the ED within 14 days

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Independent Variables: Decision Fatigue

1. Number of patients seen by the physician prior to the indexedpatient’s arrival

2. Hours relative to the shift beginning

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Table: Summary statistics of outcome variables

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Table: Summary statistics

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Graphic Evidence

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Figure: Physician decisions over the number of previous cases

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Regression Specifications

The baseline regression describing the association between fatigue andphysician decisions is as follows:

Yijt = Fatigueijtα+Xiβ + Ttγ + νj + εijt (1)

• Yijt - decisions for or outcome of patient i, treated by physician jwith consultation start time t

• Fatigueijt - number of cases seen by physician j prior to patienti’s arrival

• Xi - patient characteristics including gender, age (in quadraticform), and PACS index

• Tt - time fixed effects: hour of day, day of week and month-yearinteractions

• νj - physician fixed effects

• standard errors clustered at the physician level

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

OLS Results

Table: Fatigue on physician decisions

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Identification

Is the assignment of patients to physicians random?

• patient side

• physician side

• hospital administrators

IV: number of hospital ambulance arrivals

• an important determinant for the physician’s workload

• plausibly exogenous to the underlying health of a given patient

→ isolate the causal effect of workload on physician decision making

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Identification

Is the assignment of patients to physicians random?

• patient side

• physician side

• hospital administrators

IV: number of hospital ambulance arrivals

• an important determinant for the physician’s workload

• plausibly exogenous to the underlying health of a given patient

→ isolate the causal effect of workload on physician decision making

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

2SLS Results

Table: Fatigue on physician decisions

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Break within the Shift

Mental resources might be replenished by a short rest which increasesglucose and leads to a positive mood (Danziger et al., 2011).

Restricted sample of visits:

• the physician is in a shift with an at-least-one-hour break (not incharge of any patient case)

• break up a shift into two distinct sessions (before and after break)

• around 10%, 23,733 visits

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Graphic Evidence

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Figure: Physician decisions over the number of previous within-session cases

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Specification 1

Yijt = α1TotalCaseij + α2Session2ij +∑m

α3mDijm

+Xiβ + Ttγ + νj + εijt

(2)

• TotalCaseij - total number of cases seen by physician j prior topatient i’s arrival

• Session2 - indicator for the after-break session

• Dm - dummies indicating the first three cases in each session

• D1 - 1st case in session 1• D2 - 2nd case in session 1• D3 - 3rd case in session 1• D4 - 1st case in session 2• D5 - 2nd case in session 2• D6 - 3rd case in session 2

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Table: Analysis using dummies for the first three decisions in a session

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Specification 2

Yijt = α1Caseij + α2Session2ij + α3Caseij ∗ Session2ij

+Xiβ + Ttγ + νj + εijt(3)

• Case - number of previous cases within the current session

• Session2 - indicator for the after-break session

• Case ∗ Session2 - interaction term between Case and Session2

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Table: Analysis of linear trend between sessions

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Robustness 1: Alternative Fatigue Measure

Alternative measure of fatigue - cumulative hours elapsed in thephysician’s shift

Yijt =∑m

αm1(dt− t(j, t)e = m) +Xiβ + Ttγ + νj + εijt (4)

• t− t(j, t): patient’s arrival time relative to shift beginning

• αm: the average effect of m hours’ work (rounded up to thenearest nonnegative integer) on physician decisions

• reference category: visits arriving more than six hours after shiftbeginning

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Table: Robustness 1 - Alternative fatigue measure

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Table: Robustness 2 - Number of patients in the ED

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Table: Robustness 3 - Include both the cumulative minutes andnumber of previous cases

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Other Robustness Checks

Restrictions on physician working hours

- Shift length:

• 8-12 hours

• 8-10 hours

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Heterogeneous Analysis 1 - Nonlinear Fatigue Effects

Yijt =∑m

αmDijm +Xiβ + Ttγ + νj + εijt (5)

Dijm - dummy indicator: number of cases seen by physician j beforepatient i’s arrival falls into group m

• D1: number of previous cases is between 0 to 2

• D2: number of previous cases is between 3 to 5

• D3: number of previous cases is between 6 to 8

• D4: number of previous cases is between 9 to 11

• D5: number of previous cases is between 12 to 14

• D6: number of previous cases is between 15 to 17

• D7: number of previous cases is between 18 to 20

• reference category: number of previous cases is larger than 20

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Nonlinear Fatigue Effects

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Figure: Parameter estimates with 95% CI 31 / 50

Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Heterogeneous Analysis 2

Consider both quantity and composition of cases seen by thephysician, before the indexed patient’s arrival

• total number of cases: overall tendency

• number of severe cases: severe cases are likely to cost much morephysician effort in terms of concentration and medical inputs

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Table: Heterogeneous analysis 2 - number of previous severe cases

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Will physician decision fatigue increase patient risk?

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Physician Decision Fatigue and Patient Outcomes

***

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Number of previous cases below (Left) versus above (Right) the sample mean

(A) Data for PACS1&PACS2 cases. (B)Data for all cases.

Figure: Patient outcomes over the number of previous cases

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Table: Physician decision fatigue on patient outcomes

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Who Are More Responsive to Fatigue?

Physician specific fatigue effects

• obtained from IV estimations for each physician in our sample

Physician characteristics

• gender, medical credentials, graduation year and school

• collected from Singapore Medical Council (SMC)

• 104 out of 124 physicians in our sample

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Summary Statistics

Table: Physician characteristics and fatigue effects

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

.3.4

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Graphs by physician experience

Data for patients who were seen by physicians with medical experience

(A) less than 10 years; (B) 10 to 17 years; (C) over 17 years.

Figure: Proportion of discharge home over the number of previous cases39 / 50

Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

23

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Graphs by physician experience

Data for patients who were seen by physicians with medical experience

(A) less than 10 years; (B) 10 to 17 years; (C) over 17 years.

Figure: Average test orders over the number of previous cases40 / 50

Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Table: Correlates of fatigue effects and physician characteristics

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Conclusions

Research questions

• Whether and how decision fatigue affects physician behavior?

• What kind of physicians are more vulnerable to decision fatigue?

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Answers to Research Questions

Decision fatigue: Physicians show an increased tendency to

• adopt outpatient disposition especially discharge home

• reduce task orders

• shorten patient length of stay

→ increased ED mortality and return visits

Physician characteristics influence the magnitudes of fatigue effects

• medical experience: U-shaped

• gender: Male ↑

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Implications

To counteract the effects of decision fatigue

• more breaks;

• serious cases as earlier cases;

• more physicians and less choices.

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Thank you for your comments!

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Appendix Figures

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Graphs by physician experience

Data for patients who were seen by physicians with medical experience

(A) less than 10 years; (B) 10 to 17 years; (C) over 17 years.

Figure: Proportion of outpatient disposition over number of previous cases45 / 50

Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

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Graphs by physician experience

Data for patients who were seen by physicians with medical experience

(A) less than 10 years; (B) 10 to 17 years; (C) over 17 years.

Figure: Proportion of hospital admission over the number of previous cases46 / 50

Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

24

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Graphs by physician experience

Data for patients who were seen by physicians with medical experience

(A) less than 10 years; (B) 10 to 17 years; (C) over 17 years.

Figure: Average task orders over the number of previous cases47 / 50

Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

050

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Graphs by physician experience

Data for patients who were seen by physicians with medical experience

(A) less than 10 years; (B) 10 to 17 years; (C) over 17 years.

Figure: Average length of stay over the number of previous cases48 / 50

Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Appendix Tables

Table: Robustness - Restrictions on physician working hoursshift length 8 - 12 hours

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Introduction Data Empirical Analysis Who Are More Responsive to Fatigue? Conclusion

Table: Robustness - Restrictions on physician working hoursshift length 8 - 10 hours

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