This work provides theoretical and empirical evidence that in an ED, the arrival of an additional low-acuity patient substantially delays high-acuity patients even under the preemptive policy. To estimate the effect, we propose a temporal causal structure motivated by queueing theory to estimate the cumulative extra wait times caused by one low-acuity patient on high-acuity patients. A stylized queueing model mimicking ED operations is built to provide robustness checks for our estimation framework, moreover, under which, we provide theoretical lower bounds for the extra wait times to understand better the delaying process. The arrival patterns of the low- and high-acuity patients and the transition delay process mainly characterize the delay. We recommend hospitals to do more on diverting low-acuity patients to other channels even before the system get congested. When assessing the quality of care in an ED, policymakers should pay more attention to the metrics looking at the externality imposed by low-acuity patients. Evidence from a quasi-randomization in a wait-time forecast field experiment provides insights on how to mitigate such delay.
PhD Program, Operations, Information & Technology
PhD Program Office
Graduate School of Business
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Stanford, CA 94305
Healthcare Operations Management
Behavioral Operations Management
Service Operations Management
Decision Making under Uncertainty
In the field experiments that we are currently running in our main partner hospital, we examine the impact of wait time information provision on patients' waiting experiences, related behaviors, and health outcomes, and how best to communicate such information to benefit patients. To start, we experiment with three different wait time information to low-acuity patients. Through an incentivized survey, patients can electronically self-report their real-time satisfaction on wait times and level of pain throughout their service in the Emergency Department and after. Matching patients' responses with their electronic medical report data and the NRC health data collected by the hospital, we can identify the impact of different wait time information on patients' waiting satisfaction, left without being seen behavior, pain level, and health outcomes.
This paper demonstrated that let students to play with a simulator mimicking the actual college admission process, assuming all students adopt active learning methods, can help them rationalize their school choice decisions. Consequently, from a social welfare perspective, the number of un-matched open positions in the centralized school system decreases. Under different utility functions, the simulation results show that the number of wasted un-matched open spots decreases to the same level (around 18 seats out of 1000). The speed of decreasing can be different; the more conservative the utility function is, the faster the number of open positions decreases. Overall, students strategy will converge to optimal by participating in the simulation game.
This paper investigated how the short and long term behavior of the disease invasion can be influenced by heterogeneity. We Looked at heterogeneities in terms of the number of subpopulations and stages of infection. We evaluated quantitative differences in the characteristics of an epidemic in a simple homogeneous population model versus heterogeneous models, and we revealed the conditions under which heterogeneity could be ignored.
Last Updated 7 Nov 2019