Breast cancer is the most common non-skin cancer and the second leading cause of cancer death in U.S. women. Early detection and timely intervention are thus critical in reducing breast cancer-related deaths. Existing literature for the design of personalized mammography screening is mainly concerned with modeling the problem as a partially observable Markov decision process, which are computationally difficult to solve. In this study, we propose a machine learning-based approach for identifying the personalized screening recommendations using medical history and associated risk factors for individual patients. We find that machine learning models could provide a high degree of accuracy at drastically reduced computational complexity. Furthermore, once trained to sufficient accuracy, we ascertain explainable insights into machine learning model decisions. These insights yield a set of actionable decision rules that healthcare providers could use to support informed patient screening decisions. Overall, our study showcases the potential of machine learning in providing accurate and actionable recommendations for breast cancer screening.

The COVID-19 pandemic has strained global health systems, exacerbating health disparities, especially among vulnerable groups. It has also worsened mental health, leading to increased rates of depression and anxiety. We study the impact of the COVID-19 pandemic on the prevalence of mental health episodes involving violence in Ontario, the largest province of Canada. We compare the dangerousness of mental health patients who needed hospitalization before and during/after the pandemic across different socio-demographic groups and geographic regions. This enables us to identify the vulnerable populations in this domain as well as the key factors associated with disparities among patients at risk of exhibiting aggression. We conducted a retrospective study from March 2017 to March 2023. The study involved 340,000+ observations from patients aged 15 and above admitted to mental health inpatient hospital wards in Ontario, Canada. We evaluated violent behavior using three mental health indicators, including the risk of harming others, hospital admissions due to threats or danger to others, and history of police intervention for violent behavior within the last 30days. We also examined associated disparities across several social determinants of health through a combination of absolute rate analysis, logistic regression, stratified autoregressive integrated moving average models, and Oaxaca-Blinder decomposition. Our findings indicated a pre-existing and noteworthy increase in violent behavior among patients with mental health conditions after the onset of the pandemic. Males, young and middle-aged adults, unmarried individuals, and low-income demographics suffered from the widening gap. The disparities were most evident in urban areas, and less educated groups showed higher levels of violent behavior. Policy announcements, such as school closures, had a substantial impact on mental health disparities, resulting in lasting effects on mental well-being. The COVID-19 pandemic has worsened mental health disparities related to violence, necessitating targeted interventions and policies to improve mental health outcomes and reduce violence-related health inequities.

While prior research on inpatient care costs has primarily focused on patient- and clinical-level factors, limited empirical attention has been given to how physician collaboration shapes cost outcomes. Few studies have examined this relationship using social network analysis at the micro level. This study investigates how collaboration networks influence care costs, the mechanisms through which these effects occur, and the moderating role of attending physicians' workload. The structure of collaboration networks determines how efficiently information is shared and decisions are made, which in turn influences healthcare costs. Physicians' centrality within the network impacts their ability to access information and facilitate knowledge transfer, with higher centrality promoting better collaboration, reducing redundancies, and improving decision-making. Using digital trace data from a hospital in China, we employed social network analysis to identify collaborative networks and fitted a log-linear model to examine the association between these networks and healthcare costs. The results demonstrate that degree and closeness centrality of the attending physicians are negatively correlated with hospitalization cost. In contrast, betweenness centrality was found positively correlated with hospitalization cost. Additionally, we find that centrality metrics help reduce diagnostic and treatment costs by enhancing information exchange and clinical decision-making. Furthermore, the workload of attending physicians significantly impacted the relationship between collaboration network centrality and care costs. Specifically, the combined effect of an attending physician's degree and workload has an additional negative impact on hospitalization costs. The interaction between betweenness centrality and workload was found to be positively correlated with hospitalization costs. As the healthcare industry continues to evolve towards more collaborative and integrated models, these findings contribute to guiding effective and cost-efficient healthcare delivery.

There is an ever-increasing need for hospitals in the United States to improve upon their performance. In particular, it is necessary for hospitals to decrease their costs while improving patient satisfaction. Intuitively, hospitals adopt different strategies to accomplish those goals. Researchers have examined how hospitals that use a focus strategy (i.e., specialization) seek ways to improve performance by increased efficiencies and coordination among resources. Other studies examine the impact of increased hospital services (i.e. breadth) as a means to benefit from economies of scope. This study expands upon those literatures by submitting that focus and breadth do not have to be opposing strategies but can be implemented simultaneously; i.e. breadth of services with specialized focus on a few. The current study also examines how hospital size moderates the relationship between those two hospital strategies and performance. Specifically, this study applies an organizational information processing theory lens to predict that hospital focus and service breadth will impact patient satisfaction and cost per discharge, and how those relationships will be moderated by hospital size. Using a pooled cross-section, a regression analysis shows that hospital focus generally improves patient satisfaction while lowering cost; however, the impact on patient satisfaction is diminished for large hospitals. Additionally, service breadth tends to decrease patient satisfaction and lowers cost per discharge; however, the decrease in patient satisfaction is partially mitigated for large hospitals.

Harnessing the synergy between artificial intelligence (AI) and operations research (OR) helps drive efficiency, safety, and innovation in biomanufacturing. AI offers predictive capabilities, while OR represents the pinnacle of prescriptive analytics. AI and OR complement each other by offering structured, interpretable, and verifiable solutions to complex operational challenges. In this commentary, we reflect on how to realize the full potential of AI-OR implementations in biomanufacturing. We elaborate on recent university-industry partnerships demonstrating these benefits and propose a roadmap for AI-OR integration in biomanufacturing.

Community Health Houses (CHHs) are new entities in the Italian National Health Service that have been envisaged to provide proximity care to an increasingly aging population, and bear some similarities to other facilities in countries that have historically focused on public healthcare. This work proposes an integrated decision support system (DSS) for their planning, envisioned during the aftermath of the COVID-19 pandemic, which highlighted the frailty of the existing system. The DSS is based on an integer linear programming (ILP) model that simultaneously makes location, districting and dimensioning decisions for CHH, and accounts for accessibility and equity requirements. Based on Italian law yet designed in a parametrized way that makes it adaptable to several contexts, the DSS is able to design a hub and spoke network, which considers the provision of both mandatory and additional services. The sizes of the former are determined by directly taking into account the population served, while those of the latter are determined according to the specific demand for these services, accounting for diverse needs arising from different territories. The DSS also uses territorial units that refer to recognizable administrative areas. This ensures that the districting is easily recognized and accepted by the population. In addition to the ILP formulation, three decomposition-based matheuristics are proposed, which allow suitable solutions to be found within a reasonable time also for large and heterogeneous instances, while maintaining the flexibility of the ILP formulation. Computational results on synthetic realistic instances validated the DSS, while its application to a real-life case in a Northern Italian province demonstrated the effectiveness of the heuristic approaches and provided a proof of concept for its practical application.

Operating room (OR) scheduling is a critical challenge in healthcare, directly impacting patient outcomes and hospital efficiency. Traditional methods often struggle with the complex, multi-level constraints and uncertainties inherent in OR scheduling, such as resource limitations, variable surgery durations, and emergency cases. This study aims to develop a novel hybrid framework that optimizes OR scheduling by integrating multi-level optimization with reinforcement learning and column generation techniques. The proposed framework decomposes the OR scheduling problem into strategic, tactical, and operational levels, enabling focused optimization at each layer while ensuring cohesive decision-making across the hierarchy. Reinforcement learning guides the column generation process, learning policies that identify promising scheduling options to enhance solution quality and computational efficiency. Robust uncertainty handling mechanisms are incorporated to manage variability in surgery durations and resource availability without compromising tractability. Experiments were conducted using three years of real-world data from Shanxi Provincial People's Hospital, complemented by large-scale synthetic datasets to evaluate scalability and robustness of the framework. The framework demonstrates meaningful improvements in key operational metrics compared to traditional approaches. Analysis of three years of implementation shows consistent enhancements in operational efficiency, including a reduction in average patient waiting time by 15.8% (from 10.1 to 8.5 days), an increase in OR utilization by 5.4 percentage points (from 73.8% to 79.2%), and improved workload balance among surgeons. The framework maintains robust performance under uncertainty, achieving a 92.5% feasibility rate and reducing schedule disruptions by 26.2%. The proposed hybrid framework offers a practical and scalable solution for optimizing OR scheduling, demonstrating improvements in healthcare delivery and operational performance in real hospital environments. By effectively balancing multiple operational objectives while handling practical constraints and uncertainties, the framework provides a viable approach for healthcare systems seeking incremental yet sustainable improvements in efficiency and patient care.

Motivated by our collaboration with a residency program at an academic health system, we propose new integer programming (IP) approaches for the resident-to-rotation assignment problem (RRAP). Given sets of residents, resident classes, and departments, as well as a block structure for each class, staffing needs, rotation requirements for each class, program rules, and resident vacation requests, the RRAP involves finding a feasible year-long rotation schedule that specifies resident assignments to rotations and vacation times. We first present an IP formulation for the RRAP, which mimics the manual method for generating rotation schedules in practice and can be easily implemented and efficiently solved using off-the-shelf optimization software. However, it can lead to disparities in satisfying vacation requests among residents. To mitigate such disparities, we derive an equity-promoting counterpart that finds an optimal rotation schedule, maximizing the number of satisfied vacation requests while minimizing a measure of disparity in satisfying these requests. Then, we propose a computationally efficient Pareto Search Algorithm capable of finding the complete set of Pareto optimal solutions to the equity-promoting IP within a time that is suitable for practical implementation. Additionally, we present a user-friendly tool that implements the proposed models to automate the generation of the rotation schedule. Finally, we construct diverse RRAP instances based on data from our collaborator and conduct extensive experiments to illustrate the potential practical benefits of our proposed approaches. Our results demonstrate the computational efficiency and implementability of our approaches and underscore their potential to enhance fairness in resident rotation scheduling.