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Abstract This study implemented and evaluated a prediction-driven nurse staffing framework in a large adult emergency department. The framework leveraged a two-stage prediction model that forecasted patient volume and guided staffing decisions. Using a pre-post study design, we compared patient throughput (measured by door-to-evaluation time, active treatment time, boarding time, length of stay, and left-without-being-seen rate) and cost outcomes (measured as hourly nurse staffing costs) before and after implementation. The model achieved an RMSE of 11.261 and MAPE of 13.414% at the base stage, and an RMSE of 9.973 and MAPE of 12.126% at the surge stage. The framework reduced hourly staffing costs by $162.04 without negatively affecting throughput. Reducing one nurse per hour from the recommended level increased wait times by two minutes, with an additional 2.3-min increase when staffing dropped below 20% of recommendations. These findings highlight the potential of prediction-driven staffing to reduce costs while maintaining patient throughput. 

Abstract IntroductionIn order to be positioned to address the increasing strain of burnout and worsening nurse shortage, a better understanding of factors that contribute to nursing workload is required. This study aims to examine the difference between order‐based and clinically perceived nursing workloads and to quantify factors that contribute to a higher clinically perceived workload. DesignA retrospective cohort study was used on an observational dataset. MethodsWe combined patient flow, nurse staffing and assignment, and workload intensity data and used multivariate linear regression to analyze how various shift, patient, and nurse‐level factors, beyond order‐based workload, affect nurses' clinically perceived workload. ResultsAmong 53% of our samples, the clinically perceived workload is higher than the order‐based workload. Factors associated with a higher clinically perceived workload include weekend or night shifts, shifts with a higher census, patients within the first 24 h of admission, and male patients. ConclusionsThe order‐based workload measures tended to underestimate nurses' clinically perceived workload. We identified and quantified factors that contribute to a higher clinically perceived workload, discussed the potential mechanisms as to how these factors affect the clinically perceived workload, and proposed targeted interventions to better manage nursing workload. Clinical RelevanceBy identifying factors associated with a high clinically perceived workload, the nurse manager can provide appropriate interventions to lighten nursing workload, which may further reduce the risk of nurse burnout and shortage. 

Stochastic Gradient Descent with Adaptive Data Stochastic gradient descent (SGD) is a central tool in modern optimization, but its classical theory relies on the assumption that data are independent of the decisions being optimized. In many operations research settings, this assumption fails: policies influence system dynamics, and the resulting data feed back into subsequent updates. In “Stochastic Gradient Descent with Adaptive Data,” Che, Dong, and Tong address this challenge by developing a general framework for analyzing SGD when data are generated adaptively by policy-dependent Markov processes. Their analysis shows that fully adaptive SGD can still attain convergence rates comparable to the classical i.i.d. setting, provided the underlying system satisfies mild ergodicity and continuity conditions. The theory is illustrated through canonical applications in operations research and reinforcement learning. Overall, the paper provides rigorous and reassuring theoretical foundations for deploying learning algorithms in dynamic environments where decisions and data are fundamentally intertwined. 

Queue scheduling, in which limited resources must be allocated to incoming customers, has numerous applications in service operations management. With increasing data availability and advances in predictive models, personalized scheduling—which leverages individual information about underlying stochastic processes beyond just probability distributions—has gained significant attention. A new study reveals that, even with noisy service-time predictions, the (predicted) shortest-job-first (SJF) policy can effectively optimize performance in many-server systems with inpatient customers. The study also characterizes the impact of prediction errors on the policy’s effectiveness. Additionally, the study shows that a two-class priority rule, in which customers with shorter predicted service times (below a carefully designed threshold) are prioritized, can asymptotically match the performance of SJF, offering a simpler policy for implementation in practice. 

In many operations management problems, we need to make decisions sequentially to minimize the cost, satisfying certain constraints. One modeling approach to such problems is the constrained Markov decision process (CMDP). In this work, we develop a data-driven primal-dual algorithm to solve CMDPs. Our approach alternatively applies regularized policy iteration to improve the policy and subgradient ascent to maintain the constraints. Under mild regularity conditions, we show that the algorithm converges at rate [Formula: see text], where T is the number of iterations, for both the discounted and long-run average cost formulations. Our algorithm can be easily combined with advanced deep learning techniques to deal with complex large-scale problems with the additional benefit of straightforward convergence analysis. When the CMDP has a weakly coupled structure, our approach can further reduce the computational complexity through an embedded decomposition. We apply the algorithm to two operations management problems: multiclass queue scheduling and multiproduct inventory management. Numerical experiments demonstrate that our algorithm, when combined with appropriate value function approximations, generates policies that achieve superior performance compared with state-of-the-art heuristics. This paper was accepted by Baris Ata, stochastic models and simulation. Funding: Y. Chen was supported by the Hong Kong Research Grants Council, Early Career Scheme Fund [Grant 26508924], and partially supported by a grant from the National Natural Science Foundation of China [Grant 72495125]. J. Dong was supported by the National Science Foundation [Grant 1944209]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03736 . 

Determining emergency department (ED) nurse staffing decisions to balance quality of service and staffing costs can be extremely challenging, especially when there is a high level of uncertainty in patient demand. Increasing data availability and continuing advancements in predictive analytics provide an opportunity to mitigate demand uncertainty by using demand forecasts. In this work, we study a two-stage prediction-driven staffing framework where the prediction models are integrated with the base (made weeks in advance) and surge (made nearly real-time) nurse staffing decisions in the ED. We quantify the benefit of having the ability to use the more expensive surge staffing and identify the importance of balancing demand uncertainty versus system stochasticity. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Last, we develop a unified framework that combines parameter estimation, real-time demand forecasts, and nurse staffing in the ED. High-fidelity simulation experiments for the ED demonstrate that the proposed framework has the potential to reduce annual staffing costs by 10%–16% ($2 M–$3 M) while guaranteeing timely access to care. This paper was accepted by David Simchi-Levi, healthcare management. Funding: J. Dong was partially supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant CMMI-1944209]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.02781 . 

When having access to demand forecasts, a crucial question is how to effectively use this information to make better resource allocation decisions, especially during demand surges like the COVID-19 pandemic. Despite the emergence of various advanced prediction models for hospital resources, there has been a lack of prescriptive solutions for hospital managers seeking concrete decision support, for example, guidance on whether to allocate beds from other specialties to meet the surge demand from COVID-19 patients by postponing elective surgeries. In their paper “Optimal Routing under Demand Surge: the Value of Future Arrival Rate,” the authors present a systematic framework to incorporate future demand into routing decisions in parallel server systems with partial flexibility and quantify the benefits of doing so. They propose a simple and interpretable two-stage index-based policy that explicitly incorporates demand forecasts into real-time routing decisions. Their analytical and numerical results demonstrate the policy’s effectiveness, even in the presence of large prediction errors. 

Service systems are typically limited resource environments where scarce capacity is reserved for the most urgent customers. However, there has been a growing interest in the use of proactive service when a less urgent customer may become urgent while waiting. On one hand, providing service for customers when they are less urgent could mean that fewer resources are needed to fulfill their service requirement. On the other hand, using limited capacity for customers who may never need the service in the future takes the capacity away from other more urgent customers who need it now. To understand this tension, we propose a multiserver queueing model with two customer classes: moderate and urgent. We allow customers to transition classes while waiting. In this setting, we characterize how moderate and urgent customers should be prioritized for service when proactive service for moderate customers is an option. We identify an index, the modified [Formula: see text]-index, which plays an important role in determining the optimal scheduling policy. This index lends itself to an intuitive interpretation of how to balance holding costs, service times, abandonments, and transitions between customer classes. This paper was accepted by David Simchi-Levi, stochastic models and simulation.