Split liver transplantation (SLT) can potentially save two lives using one liver. To facilitate increased SLT usage, we formulate a multi-queue fluid model, incorporating size matching specifics, dynamic health conditions, transplant type, and fairness. We find the optimal organ allocation policy, and evaluate its performance versus other common allocations.

We study the deceased-donor liver allocation policies in the United States. In the transplant community, broader organ sharing is believed to mitigate geographic inequity in organ access, and recent policies are moving in that direction in principle. The key message to policymakers is that they should move away from the `one-size-fits-all' approach and focus on matching supply and demand to develop organ allocation policies that score well in terms of efficiency and geographic equity.

Problem: We enhance a hospital's abdominal surgery efficiency using patient-specific information. Our framework addresses heterogeneous surgical volume effects and generates patient-specific data. Regression models, causal forest, and optimization reveal significant effects and reduce surgery duration by 3-18%. This improves efficiency by matching patients to surgeons based on specific volume effects.

Physicians spend more than 5 hours a day on EHR and 1 hour after work, causing burnout, attrition, and appointment delays. This paper examines the impact of workflow decisions on EHR time. Using 150,000 appointments from 74 family medicine physicians, we find pre-appointment EHR reduces workload and after-work hours, while post-appointment EHR decreases after-work hours but increases total EHR time. Our findings help healthcare administrators design EHR workflows to reduce physician burnout.

We study how task switching in emergency departments (ED) impacts physician efficiency, quality of care, and patient routing. We find task switching hurts physician productivity, while its influence on quality is insignificant. ED physicians are switch-averse when routing patients. Leveraging the heterogeneity among task switches, we propose an implementable data-driven queue management method to partition patients into two queues. The simulation shows our method effectively improves efficiency.

This study investigates the impact of telehealth expansion on rural-urban healthcare access disparities during COVID-19. The findings reveal an increased gap between rural and urban areas, with telehealth contributing to a 3.9% rise in the disparity. Urban patients adopt telehealth more, while rural patients rely on in-person visits. The research highlights the need to address barriers and promote equitable access to remote care, informing policymakers, healthcare providers, and researchers.

Current trends suggest telemedicine will continue to play a key role in post-pandemic care delivery. Some empirical studies, however, observed that adopting telemedicine can trigger more demand for in-person visits and overcrowd the clinic. We develop a queueing game model to assess the impact of telemedicine in equilibrium. Analyzing this model allows us to characterize the optimal resource allocation for outpatient clinics and the conditions under which introducing telemedicine is beneficial.

We describe a novel approach to automatically generating data-driven simulation models from event log data by combining process mining, queue mining, and machine learning techniques. The resulting model can be used for mapping and improving the process. We describe a healthcare application of this technique where the focus is on estimating direct and indirect effects of congestion. We also discuss how to overcome a challenge posed by very scarce event log.

Problem: Impact of telehealth on patient behaviors (no-shows, unpunctuality) unclear. Analysis of 280,067 appointments shows telehealth reduces no-shows by 4.0% and late arrivals by 10.2%. Adherence improves for follow-up patients (convenience) and new patients (timely access). Improved adherence enhances throughput, efficiency, and access to care. Telehealth implementation justified for increased revenue.

While the cost-saving benefit of generic drugs is obvious, the treatment effectiveness remains unclear. We examine the effect of generic drug usage on health outcomes and address the potential endogeneity concern using instrumental variables and a difference-in-differences framework. We find that generic drug usage leads to higher healthcare service utilization and worse clinical outcomes. Moreover, Our findings highlight the effectiveness heterogeneity of generics from different manufacturers.

Problem: We examine telemedicine's impact on antibiotic prescription errors using patient-provider encounter data. Two-stage least squares regressions show lower overall errors with telemedicine. Effects vary by provider's patient volume and relationship. Reduction in errors mainly stems from type I errors, without compromising patient health outcomes. Telemedicine reduces drug waste and antibiotic resistance, benefiting decision-makers and patients.

The use of telemedicine has grown rapidly. To understand patient behaviors in telemedicine and in-person visits, we studied service incompletion. Physician availability affects in-person visits but not no-shows. Using a multivariate probit model, we found that intra-day delay increases telemedicine service incompletion by 7.40% but has no significant effect on in-person visits. Differentiating incompletions from no-shows is crucial for optimal patient sequencing decisions.

Sepsis is a life-threatening response to infection that leads to organ failure, tissue damage, and oftentimes, death. Our work leverages historical health data in order to learn treatment strategies for sepsis that result in improved patient outcomes under limited resources. We learn an interpretable, Markov Decision Process (MDP) model of the system, formulate a robust value iteration algorithm, and solve the problem of limited resource allocation for optimal sepsis treatment.

Problem: ED overcrowding disrupts public safety net. Impact of increasing ED entry fee is unknown.

Methodology/Results: In Hong Kong, increasing the ED fee reduced traffic by 6.3%, targeting less-urgent visits. Frequent visitors decreased their visits, reducing patient abandonment.

Managerial implications: Financial access hurdles alleviate healthcare congestion without discouraging urgent visits. Managing external demand complements internal process improvements in ED management.

In many service systems, service times of customers can be correlated with waiting times. Scheduling under such dependency is challenging as a Markovian state description requires keeping track of all customers' waiting history. We propose an approximate dynamic programming algorithm for multi-class scheduling with wait-dependent service times. Our algorithm can generate policies with simple structures and achieve strong performance which we illustrate in a healthcare setting using real data.

Many pharmaceutical drugs have multiple indications, for which they offer a varying degree of benefit for patients. Yet, in the current US pricing system, the price of the drug is the same regardless of the indication for which it is prescribed. We use a modeling approach to analyze how indication-based pricing compares to uniform pricing for the manufacturer’s profit and investment incentives, the patients’ access to the drug and benefit, and the payer’s coverage incentives and objective.

We propose and analyze a budget-neutral incentive scheme that Organ Procurement Organizations could utilize to increase donor hospital's (DH's) proportion of timely referrals and thereby increase the number of potential donors. We find that, depending on the DH's cost of effort required to increase the proportion of timely referrals, the proposed contract could increase the percentage of family authorizations from a single DH by 1.3%.

Although available evidence suggests that home hemodialysis(HHD) may achieve similar clinical outcomes to in-center hemodialysis and are less resource intensive for patients with end-stage renal disease, they have been used less in the US than in other developed nations. We design two incentive plans in a bi-level game structure consisting of a payer and providers to obtain equilibrium. We show these incentive plans can improve the HHD rate and increase ESRD beneficiaries' quality of life.

Patient-level data from 72 MRI hospitals in Ontario, Canada from 2013 to 2017 shows that over 60% of patients exceeded their wait time targets. We conduct a data-driven analysis to quantify the reduction in the patient Fraction Exceeding Target (FET) for MRI. Our resource pooling model lowers the FET from 66% to 36% while constraining the average incremental travel time below three hours. In addition, our model and method show that only ten additional scanners are needed to achieve 10% FET.

Problem: We optimize personalized interventions for behavioral health using digital platforms, considering cost and capacity constraints. Existing approaches are data-intensive or overlook long-term dynamics. Our DecompPI algorithm approximates policy iteration, reducing intervention costs while maintaining efficacy. A case study shows potential for 50% cost reduction, enabling scalable implementation.

Many developing countries lack the health-emergency infrastructure of the developed world. In this context, our industry partner Flare (operating in Nairobi, Kenya) coordinates existing ambulance providers by operating a platform. We study the operational challenges for such platforms as they often lack knowledge about all ambulances' future availability and their location at a tactical level and typically do not fully control these ambulances.

Problem: Inpatients wait for hospital diagnostic services, causing chaos. Methodology: We propose "advance notice" scheduling, providing patients with preparation and service time windows. Markov Decision Process optimizes decisions. Numerical study shows significant operational improvement. Managerial implications: Advance notice policy reduces waiting and improves appointment-based services.

Problem: Evaluating an ED delay announcement system in Hong Kong's healthcare. Methodology: Studying 1.3M patient visits, we estimate patient sensitivity to announced waiting times (WT) and factors influencing it. Results show potential improvements in reducing WT and patients leaving without being seen. Implications: Increase patient awareness, reduce WT update window, focus on older population and Kowloon district for promotion.

We study hospitals' operational characteristics contributing to the re-admission of psychiatry patients. We propose that length of stay (LOS) mediates the effects of hospital characteristics on the risk of readmission. We reveal how patient characteristics moderate these effects. Using a dataset of 15,000 psychiatry patients, we provide evidence on the negative volume-outcome and nonlinear LOS-outcome relationship. Our analysis provides helpful insights for managing the flow of psychiatric patients.

We investigate the crucial and complex task of selecting patients by physicians in emergency departments when multiple patient types are present. Conventional approaches in guiding physician decision-making are based on patient triage levels or waiting times. However, an important factor that is often overlooked is the time remaining in a physician's shift. We utilize a transient optimal control on the corresponding queueing system to derive an optimal time-dependent patient selection strategy.

Given heterogeneous treatment time distributions, no-shows, and patients that can arrive early or late, we show how a discrete time Markov chain model can be used to equitably space patients so that no patient has an expected wait longer than some maximum value. The calculation of an equitable schedule can be done extremely fast, in polynomial time, involving only simple linear algebra (matrix multiplication). The equitable schedule adds at most 2% to the clinic’s operation time.

Emergency Department (ED) boarding refers to the delay in transfer experienced by admitted patients from the ED to inpatient units. Using an instrumental variable design, we found that, on average, longer boarding time leads to a longer hospital stay and a higher chance of care escalation. Our findings also reveal that the impact of boarding differs across patients, suggesting that considering such heterogeneity when assigning inpatient beds could improve downstream efficiency and quality of care

The US heart transplant system prioritizes candidates based on the severity of their pre-transplant therapy; the premise is that this severity reflects medical urgency. However, it is widely suggested that this rule opens room for gaming the system, by assigning high-severity therapies to low-urgency patients. We study a novel model of the gaming decisions of heart transplant centers. We identify the underlying trade-offs and shed light on the conditions that give rise to such gaming.

In this paper, our goal is to (i) characterize the equilibrium behaviour of the agents under targeted priority mechanisms, (ii) identify the impact of these mechanisms on several performance metrics, including the discard rates of organs, and overall social welfare, and (iii) establish the impact of the selection of design parameters on different outcomes in equilibrium to investigate the optimal design of such mechanisms.

We optimize blood donor incentivization to reduce shortages and wastage. Our model considers random demand, perishability, and donor variability. Using a coupled queueing network approach and the Pipeline Queue paradigm, we derive a tractable convex reformulation. Results show improved performance compared to the threshold policy. It provides decision support for dynamic donor incentivization in blood supply chain management.