Admission Control Bias and Path-Dependent Feedback Under Diagnosis Uncertainty

Song-Hee Kim () and Jordan Tong ()
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Song-Hee Kim: SNU Business School, Seoul National University, Seoul 08826, Korea
Jordan Tong: Wisconsin School of Business, University of Wisconsin–Madison, Madison, Wisconsin 53706

Manufacturing & Service Operations Management, 2024, vol. 26, issue 1, 117-136

Abstract: Problem definition : Do physicians exhibit predictable behavioral bias when making admission control decisions under patient diagnosis uncertainty with stochastic arrivals and lengths of stay? How can we structure feedback to help improve their decision making? Methodology/results : We use a behavioral model to theorize how a diagnosis anchoring and insufficient adjustment heuristic may lead to an over-rationing bias, and we hypothesize when this bias is greatest. We then propose that feedback for rejected patients—above and beyond feedback for admitted patients—is critical for mitigating this bias. This is because feedback for only admitted patients may suffer from a type of path dependency that prevents decision makers from receiving the most helpful disconfirming feedback. We provide evidence supporting these hypotheses using preregistered experiments in which medical students, Amazon Mechanical Turk workers, or Prolific workers manage admissions for simulated hospital units. Managerial implications : Our results (1) illuminate an important anchoring bias in admission control under diagnosis uncertainty, (2) identify rejected-patient feedback as a critical component for mitigating this bias, and (3) provide insight into the circumstances under which these phenomena are likely to be most significant.

Keywords: healthcare operations; behavioral operations; laboratory experiments; admission control; diagnostic uncertainty; system neglect; base-rate neglect; feedback (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:1:p:117-136

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