David C. Chan

Assistant Professor of Medicine
Stanford University




Teamwork and Moral Hazard: Evidence from the Emergency Department
Journal of Political Economy, May 2016
How does teamwork increase productivity? Considering teamwork as joint monitoring and management, I investigate this question by studying the same emergency physicians working in two organizational systems differing in the team-management of work: Physicians are assigned patients in a "nurse-managed" system but divide patients between themselves in a "self-managed" system. The self-managed system increases throughput productivity by reducing a "foot-dragging" moral hazard, in which physicians prolong patient stays with expected future work. I find evidence that physicians in the same location have better information about each other and that, in the self-managed system, they use this information to assign patients.
Appendix A
Appendix B

The Efficiency of Slacking Off: Evidence from the Emergency Department
Econometrica, May 2018
Work schedules play an important role in utilizing labor in organizations. In this study of emergency department physicians in shift work, schedules induce two distortions: First, physicians "slack off" by accepting fewer patients near end of shift (EOS). Second, physicians distort patient care, incurring higher costs as they spend less time on patients assigned near EOS. Examining how these effects change with shift overlap reveals a tradeoff between the two. Within an hour after the normal time of work completion, physicians are willing to spend hospital resources more than six times their market wage to preserve their leisure. Accounting for overall costs, I find that physicians slack off at approximately second-best optimal levels.

Industry Input in Policymaking: Evidence from Medicare
Quarterly Journal of Economics, August 2019
In setting prices for physician services, Medicare solicits input from a committee that evaluates proposals from industry. The committee itself comprises members from industry; we investigate whether this arrangement leads to regulatory capture with prices biased toward industry interests. We find that increasing a measure of affiliation between the committee and proposers by one standard deviation increases prices by 10%. We then evaluate whether employing a biased committee as an intermediary may nonetheless be desirable, if greater affiliation allows the committee to extract information needed for regulation. We find industry proposers more affiliated with the committee produce less hard evidence in their proposals. However, on soft information, we find evidence of a trade-off: Private insurers set prices that more closely track Medicare prices generated under higher affiliation.

Provider Discretion and Variation in Resource Allocation: The Case of Triage Decisions
American Economic Review Papers and Proceedings, May 2020
One of the most challenging environments in health care is the emergency department. A key decision maker in that context is triage nurses who assess patient illness severity and influence wait times for medical attention. We gather novel data on the triage process across 108 EDs, including wait times, triage nurse identities and assessments, and detailed patient information and outcomes. Using quasi-random assignment to ED, we find a striking rate of “inversions” where patients who are sicker based on either ex ante information or ex post outcomes are scored as sicker and wait longer than their healthier counterparts.

Influence and Information in Team Decisions: Evidence from Medical Residency
American Economic Journal: Economic Policy, Forthcoming
Draft: February 2020
I study team decisions among physician trainees. Exploiting a discontinuity in team roles across trainee tenure, I find evidence that teams alter decision-making, concentrating influence in the hands of senior trainees. I also demonstrate little convergence in variation of trainee effects despite intensive training. This general pattern of trainee effects on team decision-making exists in all types of decisions and settings that I examine. In analyses evaluating mechanisms behind this pattern, I find support for the idea that significant experiential learning occurs during training and that teams place more weight on judgments of senior trainees in order to aggregate information.

Selection with Variation in Diagnostic Skill: Evidence from Radiologists
Revision requested at Quarterly Journal of Economics
Draft: February 2020
Physicians, judges, teachers, and agents in many other settings differ systematically in the decisions they make when faced with similar cases. Standard approaches to interpreting and exploiting such differences assume they arise solely from variation in preferences. We develop an alternative framework that allows variation in both preferences and diagnostic skill, and show that both dimensions are identified in standard settings under quasi-random assignment. We apply this framework to study pneumonia diagnoses by radiologists. Diagnosis rates vary widely among radiologists, and descriptive evidence suggests that a large component of this variation is due to differences in diagnostic skill. Our estimated model suggests that radiologists view failing to diagnose a patient with pneumonia as more costly than incorrectly diagnosing one without, and that this leads less-skilled radiologists to optimally choose lower diagnosis thresholds. Variation in skill can explain 44 percent of the variation in diagnostic decisions, and policies that improve skill perform better than uniform decision guidelines. Failing to account for skill variation can lead to highly misleading results in research designs that use agent assignments as instruments.

Mastering the Art of Cookbook Medicine: Machine Learning, Randomized Trials, and Misallocation
Draft: July 2020
The application of machine learning (ML) to randomized controlled trials (RCTs) can quantify and improve misallocation in healthcare. We study the decision to prescribe anticoagulants for atrial fibrillation patients; anticoagulation reduces stroke risk but increases hemorrhage risk. We combine observational data on treatment choice and guideline use with ML estimates of heterogeneous treatment effects from eight RCTs. When physicians adopt a clinical guideline, treatment decisions shift towards the recommendation but adherence remains far from perfect. Improving guideline adherence would produce larger gains than informing physicians about guidelines. Adherence to an optimal rule would prevent 47% more strokes without increasing hemorrhages.