Research Papers

Chronic pain is an increasing public health concern that affects an estimated 50 million adults in the United States each year and contributes to substantial healthcare costs and lost productivity. We examine whether early intervention by pain management specialists can reduce surgeries and costs.

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Chronic pain is an increasing public health concern that affects an estimated 50 million adults in the United States each year and contributes to substantial healthcare costs and lost productivity. We examine whether early intervention by pain management specialists can reduce surgeries and costs. Leveraging a 2019 pilot program by insurers that eliminated copays and deductibles for three physical therapy sessions for low back pain patients across five states, we implement a novel two-stage model that employs a difference-in-differences (DiD) estimate as an instrumental variable (IV). The first stage estimates the exogenous change in the likelihood of patients receiving early pain management intervention in a DiD framework. The second stage uses the change as an IV for early pain management intervention. We find that early intervention significantly reduces healthcare utilization, costs, surgeries, and opioid reliance for patients with new-onset chronic pain. We validated our findings using insurance claims from a chronic-pain specialist network in Southern California. Our work provides large-scale causal evidence of the value of strategically shifting PM consultation in the care trajectory and underscores the critical need for providers, payers, and policymakers to restructure referral pathways and benefit designs to prioritize timely pain management specialist access.

During outbreaks of infectious diseases, hospitals encounter significant challenges in making well-informed nurse staffing decisions. The dilemma during these outbreaks involves a simultaneous increase in inpatient admissions and a notably elevated rate of nurse absenteeism caused by infections.

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During outbreaks of infectious diseases, hospitals encounter significant challenges in making well-informed nurse staffing decisions. The dilemma during these outbreaks involves a simultaneous increase in inpatient admissions during disease outbreaks and a notably elevated rate of nurse absenteeism caused by infections. The unobservable nature of nurse infection time, incubation period, and number of nurses infected but yet to show symptoms adds complexity to understanding when and how nurses are infected. Lack of this critical information restricts hospital managers from implementing effective and informed operational strategies and staffing plans, limiting their ability to proactively address the staffing crisis during an outbreak. We develop a dynamic random graph model with hidden nurse health status to examine the interplay between staffing policies and infection transmission dynamics. Our model extends existing random graph frameworks by incorporating nurse health status (healthy, incubation, symptomatic) as a latent variable that is endogenously linked to the evolution of disease transmission networks. Within this framework, we design an estimation procedure that maps nurse characteristics to disease transmission rates across patient-to-nurse, nurse-to-nurse, and community-to-nurse interactions. This approach enables dynamic tracking of infection sources, locations, and timing. Using data from the IU-Health hospital system during the COVID-19 pandemic, we perform counterfactual analyses to assess the effectiveness of mitigation and staffing policies aimed at protecting nurses from infections. We find that hospitals can reduce nurse absenteeism due to infection by up to 25% through improved staffing levels and workload management. Furthermore, when establishing dedicated units for the care of infectious patients, simply isolating infected patients is insufficient; it is crucial to assign a fixed group of nurses exclusively to these patients to minimize cross-infection.

In many service industries, decision making about service scheduling often relies on assessing and prioritizing customer needs and value using professional judgment and customer data

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In many service industries, decision making about service scheduling often relies on assessing and prioritizing customer needs and value using professional judgment and customer data. Traditional scheduling models assume perfect knowledge of customer service rewards and delay costs, which is unrealistic. This paper considers the optimal scheduling problem in a multi-class queueing system where the system manager learns the reward of serving customers dynamically. We model the scheduling problem as a restless multiarmed bandit (RMAB) problem, with each customer class representing an arm characterized by queue length and the manager's belief about the reward distribution. We derive the Whittle index for each customer class. The resulting Whittle index scheduling policy which prioritizes the class of customers with the highest Whittle index. We prove that the Whittle index offers an optimal solution for a system with two customer classes-one with perfect information and one with unknown parameters-and show that it is near-optimal for more general settings numerically. Our results show that the incentive to serve a class of customers with unknown rewards increases with service rate, higher belief in rewards, arrival rate and length of wait, which contrasts with traditional models. This finding highlights that as queues grow longer, the priority for serving them increases due to extended busy periods. Furthermore, for a fixed product of service rate and reward, we find that customer classes with higher service rates provides higher incentives for learning. By understanding these dynamics, managers can better allocate resources, ensuring that longer queues, which imply greater potential delays and customer dissatisfaction, are addressed more promptly.

Many shared resources, such as hotel rooms or rental cars, require cleaning, charging, or some other operation to turn around the resources between successive customer uses.

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Many shared resources, such as hotel rooms or rental cars, require cleaning, charging, or some other operation to turn around the resources between successive customer uses. We study staffing and shift planning decisions for the turnaround service capacity to minimize the sum of customer waiting and staffing costs. Random customer departures, random customer arrivals, and worker shifts with breaks add to the managerial challenge. Using the frameworks of diminishing returns, submodularity, and M-convexity, we demonstrate analytical properties for capacity decisions in three staffing scenarios, including our primary model that focuses on shift planning. We propose a solution heuristic that efficiently provides near-optimal solutions. We illustrate the value of our model for hotel housekeeping operations using data from a large city-center hotel. Reallocating some room attendants to different shift start times, especially later in the day compared to the current practice, can effectively eliminate guest waiting after the posted check-in time. Hotels can reduce room attendant idleness and room readiness issues by departing from the common industry practice of all workers starting at 8:00 am. Simply having two shift start times in the morning may virtually eliminate waiting and help in recruiting and retaining workers.