Active Research

With Harshita Kajaria-Montag, Mary Drewes (RN, IU Health), and Alexia Oaks (RN, IU Health)
Last Update (Jun '26): Resubmitted a MAJOR REVISION to Management Science
Working Paper (as of Jun '26): SSRN Link to Working Paper

Hospitals rely on nurses to turn clinical plans into patient care, but nurses’ workload is difficult to see in real time. Most health systems track nurse-to-patient ratios as a shorthand for nursing supply relative to patient demand. Ratios, however, do not reveal the burden embedded in a particular assignment. We study the rollout of a real-time workload calculation and assignment tool at a large health system. The tool converts patient status, orders, and documentation-derived needs into expected workload and displays patient- and nurse-level workload information at the point of assignment. Using operational data from 67 inpatient units, we study how charge nurses adapt assignment decisions as real-time workload information becomes available. Moving past patient ratios requires not just better measurement, but better governance of assignment discretion. Real-time workload information can support workforce stability, but hospitals also need guardrails to ensure that a bad day does not become a bad week for nurses or for patients.

With Danqi Luo and Yong Xia (Ph.D. student)
Last Update (Jun '26): Submitted a MAJOR REVISION at Manufacturing & Service Operations Management
Working Paper: SSRN Link to Working Paper

Problem Definition: Despite emergency care guidelines that emphasize prompt diagnostic ordering, emergency physicians often evaluate multiple patients before submitting diagnostic orders. We call this practice batch ordering. Batch ordering conflicts with prompt-ordering norms because it delays diagnostic initiation for earlier patients in the sequence, but it may also reduce order-entry setup costs and synchronize diagnostic work.
Methodology/Results: We use detailed order-level data from more than 272,000 patient encounters and estimate batching effects using an instrumental-variables design that leverages within-physician persistence in prior-shift batching intensity. The central result is paradoxical: batch ordering increases turnaround time for individual diagnostic orders, yet reduces overall ED service time. We explain this pattern through two channels. First, batching changes placement timing: physicians place larger initial diagnostic bundles and fewer later orders, moving diagnostic work earlier within the visit. Second, batching improves synchronization: diagnostic results arrive in a tighter window, shortening the time to actionable information. A fork-join model shows why these forces can reduce patient service time even when individual diagnostic branches are slower.
Managerial Implications: Diagnostic ordering is both a cross-patient sequencing problem and a within-patient clinical decision. ED leaders should avoid treating batch ordering as uniformly harmful or uniformly efficient. Workflow and EHR design can preserve the coordination benefits of coalesced ordering by reducing avoidable order-entry setup costs and supporting targeted batching when waiting costs are low while discouraging batching for time-sensitive patients or under severe congestion.

With Bradley Staats
Last Update: January 2024
Working Paper:  SSRN Link
Award: Decision Science Institute, Doctoral Showcase "Best Paper" (runner-up)

Methods:  Our empirical analysis combines observations from healthcare clinics across several US states, anonymized cellphone mobility data, COVID-19 severity measures, and stay-at-home orders. A Lasso-based procedure selects instruments and generates county-level measures of individual mobility, and a random forest forecast validates the insights of our descriptive approach for traffic prediction.
Conclusion: Combining observations from multiple sources allows us to evaluate how traffic to healthcare clinics changed with willingness to travel, stay-at-home orders, and other signals of environmental safety. During our study period, though patients exercised discretion in some negative ways (e.g., foregoing care), patients also shouldered some of the burden of their own wellness. In studying patient discretion, we characterize powerful predictors of healthcare traffic, we suggest how our findings might generalize post-COVID, and we encourage researchers to continue exploring the role of patient discretion in the co-production of wellness.