With RJ Niewoehner and Yong Xia (PhD student)
Last Update: January 2026
Working Paper: SSRN Link to Working Paper
Problem Definition: Emergency physicians often evaluate multiple patients before entering diagnostic orders in the electronic health record, a practice we term batch ordering. Clinical guidelines emphasize prompt ordering after evaluation, yet batch ordering remains common. How do emergency physicians structure diagnostic ordering across patients, and how does batch ordering affect operational performance and patient outcomes?
Methodology/Results: We study batch ordering across patients using detailed order-level data from over 190,000 patient encounters across two emergency departments in a large U.S. teaching hospital system. We first characterize when physicians batch and how batching responds to operational conditions. We then estimate the effect of batch ordering on performance using an instrumental-variables design that leverages variation in peers’ batching behavior. Batch ordering responds systematically to operational frictions and is not confined to low-complexity cases. Batch ordering modestly increases diagnostic turnaround for individual diagnostic orders, but it reduces variability in diagnostic completion times and shifts ordering earlier within the visit by expanding initial bundles and reducing subsequent ordering rounds. As a result, batch ordering reduces overall patient service time, with benefits concentrated under manageable congestion and attenuated when congestion is high.
Managerial Implications: Our findings contribute to operations research by treating diagnostic ordering as a cross-patient sequencing problem, beyond within-patient ordering choices. Batch ordering can improve throughput by reducing variability in diagnostic completion, but operating conditions govern when benefits appear. More broadly, the paper highlights how frontline clinicians reshape service processes through discretionary task sequencing, with consequences for throughput and patient experience. The results suggest that ED leaders should pair clinical judgment with workflow and EHR design that lowers ordering setup costs and supports smaller, targeted batches.
With Luxi Shen
Last Update: January 2026
Working Paper: SSRN Link to Working Paper
Problem Definition: Emergency departments face persistent congestion and pre-treatment waiting, yet little is known about how pre-treatment waiting shapes physician behavior at first contact and downstream operations.
Methodology/Results: We analyze electronic medical record and cost data from two hospital systems: a U.S. teaching hospital with acuity-prioritized intake and a Chinese hospital that operates first-come-first-served within departments. Using operational timestamps, we verify intake discipline and align estimation with operational queues. We identify the causal effect of pre-treatment wait using registration-time counts of patients still ahead in the relevant queue (high-priority patients in the U.S.; same-department patients in China) and a ransomware diversion shock that increased congestion in the U.S. system. Across both settings, longer waits increase first-round diagnostic ordering (1-2 additional orders per hour in the U.S.; 1-7 across Chinese departments). Waiting also increases time devoted to the initial evaluation when front-end capacity is slack, but this time response collapses under high physician workload and high bed occupancy, while ordering expansion persists. We find no consistent improvements in disposition accuracy or 30-day revisits, but clear operational costs through longer post-wait service time and higher expenditures. A randomized vignette experiment with practicing physicians reproduces the ordering response, supporting a cross-agent spillover from patient waiting to physician decisions.
Managerial Implications: Our findings suggest that more than an access metric, pre-treatment wait serves as a critical input that shifts utilization and downstream resource use. Practical levers include improving communication during the wait and making downstream diagnostic constraints more visible at order entry so ordering decisions better reflect capacity.
With Yongyi Guo, Hongyu Shan, Zhengyuan Zhou
Last Update: October 2025
Working Paper: Upon Request
Problem Definition: In the past three years, a particularly important development in the digital advertising industry is the shift from second-price auctions to first-price auctions for online display ads. This shift immediately motivated the intellectually challenging question of how to bid in first-price auctions, because unlike in second-price auctions, bidding one's private value truthfully is no longer optimal. In this paper, we study how to adaptively bid in repeated first-price auctions under binary feedback currently used by almost all ad exchanges-where the bidder only sees whether he wins or loses the bid after each auction.
Methodology/Results: We assume others' highest bid can be modelled by $m_t = \langle \theta^*, x_t \rangle + \epsilon_t$, where $x_t \in \mathbf{R}^d$ is a context representing all the relevant features about this auction (e.g. the user features and the impression opportunity features) $\theta^*$ is an unknown vector of parameters and $\epsilon_t$ is a mean-zero random variable that has an unknown cumulative distribution function (CDF). We develop a semi-nonparametric adaptive bidding policy that achieves $\tilde{O}((d^2T)^{\frac{2\nu+1}{4\nu-1}})$ regret, when the CDF of $\epsilon_t$ is $\nu$-times continuously differentiable ($\nu \ge 2$).
Managerial Implications: Our results reveal an interesting phenomenon: as the non-parametric uncertainty component becomes smoother, the regret performance also improves. In the limit, as the CDF of $\epsilon_t$ becomes infinitely smooth, we obtain the regret bound of $\tilde{O}(d\sqrt{T})$, which is minimax optimal in the time horizon up to log factors.
Low-Acuity Patients Delay High-Acuity Patients in the Emergency Department
With Mohsen Bayati, Erica L. Plambeck, Michael Aratow
Last Update: July 2025
Working Paper: SSRN Link to Working Paper
Problem Definition: Emergency departments face persistent congestion and pre-treatment waiting, yet little is known about how pre-treatment waiting shapes physician behavior at first contact and downstream operations.
Methodology/Results: We analyze electronic medical record and cost data from two hospital systems: a U.S. teaching hospital with acuity-prioritized intake and a Chinese hospital that operates first-come-first-served within departments. Using operational timestamps, we verify intake discipline and align estimation with operational queues. We identify the causal effect of pre-treatment wait using registration-time counts of patients still ahead in the relevant queue (high-priority patients in the U.S.; same-department patients in China) and a ransomware diversion shock that increased congestion in the U.S. system. Across both settings, longer waits increase first-round diagnostic ordering (1-2 additional orders per hour in the U.S.; 1-7 across Chinese departments). Waiting also increases time devoted to the initial evaluation when front-end capacity is slack, but this time response collapses under high physician workload and high bed occupancy, while ordering expansion persists. We find no consistent improvements in disposition accuracy or 30-day revisits, but clear operational costs through longer post-wait service time and higher expenditures. A randomized vignette experiment with practicing physicians reproduces the ordering response, supporting a cross-agent spillover from patient waiting to physician decisions.
Managerial Implications: Our findings suggest that more than an access metric, pre-treatment wait serves as a critical input that shifts utilization and downstream resource use. Practical levers include improving communication during the wait and making downstream diagnostic constraints more visible at order entry so ordering decisions better reflect capacity.
With Erica L. Plambeck
Last Update: July 2023
Working Paper: Upon Request
Abstract: In a hospital that aims to have fewer patients leave the Emergency Department without being seen by a physician (LWBS), we field-tested two approaches for displaying an algorithmic prediction of low-acuity patients' wait time to see a physician. The first approach is the prediction rounded to a multiple of 10 minutes, and the second is an interval designed to communicate that the wait time could be even 20 minutes longer. Relative to the control with no wait time information, both approaches significantly reduce the likelihood of LWBS, with the interval approach being more effective. Improved waiting satisfaction, as indicated by our incentivized satisfaction survey of ED patients, and a higher anticipated wait time with the interval approach, indicated by our online experiment, may contribute to these effects. Consistent with prospect theory, we find that to the extent that patients' actual wait time exceeds the displayed wait time, they have higher likelihood to LWBS. According to emergency medicine literature, many patients with ESI level 4-5 and complaint ``dental pain” or ``medication refill” need not be in the ED. Unfortunately, our intervention is most effective at reducing LWBS by those patients.