Health Care Management Science最新文献

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.

Surgery scheduling is crucial in healthcare management, particularly in hospitals and clinics. This study tackles the elective surgery scheduling problem by integrating affinity and preferences among the surgical team's members. Although these concepts can enhance coordination and improve team performance, they remain understudied in the literature. Affinity is usually quantified as a numerical representation of compatibility between team members, and preferences denote a surgeon's interest in specific surgical resources. Existing approaches have not integrated simultaneously affinity and preferences. In addition, they use mathematical programming models that often incorporate affinity and preferences as constraints or additional objective function terms, adopting a multi-objective approach. The former can significantly reduce the number of surgeries performed, while the latter increases computational complexity. To overcome these limitations, we propose mathematical programming models with a score-based penalty approach that integrates affinity and preferences while maximizing the priority of scheduled surgeries. Our approach is evaluated against two alternative models: a baseline model without affinity or preferences and a constraint-based model that follows conventional literature, incorporating these concepts as hard constraints. We implement these models using integer linear programming and constraint programming. The results show the feasibility of considering affinity and preferences among surgical team members. This can enhance the surgical team's quality with negligible impact on the number of surgeries performed. Therefore, our approach can generate stronger human relationships among surgical team members, which could contribute positively to patient surgical outcomes, as demonstrated by some studies in the literature.

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.

Pandemics pose significant challenges, particularly in the early stages when vertical resupply chains are overwhelmed. To mitigate the impact of medical resource shortages, we develop a multi-period optimization model incorporating lateral transshipment and hospital admission to minimize the total number of infected individuals by strategically allocating regional resources in the face of complex dynamics, including endogenous hospital admission rates and pandemic spread. To capture the temporal-spatial nature of pandemics, we extend the Susceptible-Exposed-Infected-Hospitalized-Recovered (SEIHR) model by accounting for population migration. Additionally, we derive threshold-type structures for optimal resource transfers, considering factors such as pandemic dynamics, patient length of stay, and budget constraints. We also demonstrate the effectiveness of our models via numerical experiments. Our research identifies three main findings: 1) Pooling medical resources effectively reduces infections and alleviates shortages in outbreak areas. This strategy is particularly beneficial during pandemics due to self-reinforcing infection dynamics and surging demand. 2) Regions adjacent to the epicenter should exercise caution in contributing resources to avoid exacerbating infections through population migration. 3) While effective in localized outbreaks, widespread resource scarcity can limit the viability of pooling strategies, potentially leading to increased infections and fluctuating resource levels in transferring regions.

The implementation of social distancing policies is key to reduce the spread of the recent COVID-19 pandemic and future pandemics. However, their effectiveness ultimately depends on human behavior. For example, in the United States, compliance with social distancing policies widely varied in Spring 2020. What factors were associated with the observed variability in behavioral compliance with the policies? Utilizing detailed county-level data, we estimate the association between human mobility and the growth rate of COVID-19 cases across approximately 3,100 U.S. counties from January 1, 2020 to June 20, 2020. In addition, using data from U.S. presidential elections we measured how the association between mobility and COVID-19 growth rate varied as a function of county voting pattern. Our results generalize previous reports in finding a significant association between political leaning and the COVID-19 growth rate. These results highlight how it might be beneficial to consider political orientation when building models of the multivariate relationships between the spread of pandemics and public health policies intended to curb the expansion of the pandemic.

In this paper, we determine whether machine learning (ML) models created using data from the novel SARS-CoV-2 Alpha variant can prospectively predict county-level incidence of emerging variants, validated using data of the Omicron variant. We first select publicly-available sociodemographic, economic, and health-related characteristics of 3140 United States (US) counties at the time of the confirmed early US outbreak of the novel SARS-CoV-2 virus in March 2020 for analysis. Our primary result is the set of US counties that experienced the upper quartile of population-adjusted Omicron variant incidence at certain period (e.g., 100 days) after Omicron variant's appearance in the US. We show more predictive results by incorporating additional data and features (e.g., human mobility) that can be acquired dynamically after the outbreak, to improve prediction accuracy at the cost of additional waiting time and effort. Towards the goal of decision support, we aim to prospectively evaluate our models' ability to classify and rank US counties at risk. We measure their classification performance using the area under receiver operating characteristic curve score with 95% confidence intervals. We further calculate the proportion of the top counties by Omicron incidence that our models correctly identify, and compare their score with those of individual county-level features that can serve as a heuristic predictive performance baseline. Our results show that ML algorithms predict county-level Omicron variant incidence with better performance than natural heuristics that decision makers might otherwise use. More generally, historical data from the first wave of a novel pandemic can help predict the incidence of future variants and strengthen state or federal pandemic response interventions. HIGHLIGHTS: Data-driven predictive models that capture patterns from early viral variants can support policymaking related to emerging viral variants. County-level sociodemographic, health, and economic characteristics are predictive of early COVID-19 outcomes in the United States (US). Machine learning models trained on early US county-level COVID-19 outcomes are additionally predictive of county-level SARS-CoV-2 Omicron variant outcomes. County-level machine learning models can be used as a critical policymaking tool given the inevitability of novel emerging viruses.

This research leverages data from various disparate sources to examine how state-level policy distribution decisions and local, county-level population vulnerability factors likely to hinder vaccination influenced COVID-19 vaccination efforts across the United States. Unlike other nations that coordinated their responses at a national level, this study uses U.S. states and counties as individual units of analysis. This approach allows for an assessment of which policies and population attributes were most impactful in driving vaccination and ensuring efficient and equitable distribution among citizens. By focusing on the diverse strategies employed by different states in terms of (1) defining the entity responsible for distribution policy, (2) determining the groups eligible for vaccination, and (3) the timing for communication of distribution plans for vaccination, this descriptive investigation sheds light on the effectiveness of state-level interventions and contributes to a deeper understanding of how to manage large-scale public health initiatives. By identifying successful strategies and potential pitfalls, the study provides a roadmap for responding to future pandemics, ensuring that vaccination efforts can be swiftly and fairly implemented to protect public health.

Adverse events in healthcare continue to challenge hospital management practices, often resulting in avoidable patient harm and substantial financial costs. Despite technological progress and the availability of risk management tools, healthcare institutions still struggle to systematically monitor and evaluate risk dynamics over time. This study proposes a multi-criteria decision analysis framework based on the ELECTRE Tri-nC method to assess the evolution of clinical and non-clinical risks at Hospital da Luz Lisboa, a private Portuguese hospital. A panel of risk management experts evaluated twelve criteria across five years (2018-2022), enabling the classification of each quarter into one of five predefined risk categories. The model accommodates the non-compensatory nature of risk indicators and integrates expert-defined thresholds. Results reveal critical periods of heightened risk, underscoring the importance of analysing risk trends over time rather than focusing on isolated incidents. A stability analysis confirms the robustness of the weight structure and highlights the model's sensitivity to changes in the credibility threshold. Overall, the proposed approach provides healthcare decision-makers with a transparent and structured framework for retrospective risk analysis and supports the design of timely, targeted mitigation strategies. The methodology is adaptable to other hospital settings.

Trauma care services are a vital part of all healthcare-based networks as timely accessibility is important for citizens. Trauma care access is even more relevant when unexpected events, such as the COVID-19 pandemic, overload the capacity of the hospitals. Research literature has highlighted that access to trauma care is not even for all populations, especially when comparing rural and urban groups. Traditionally, the focus in trauma systems was on the designation and verification of individual hospitals as trauma centers, rather than on the overall configuration of the system. Recognition of the benefits of an inclusive trauma system has precipitated a more integrated approach. The optimal geographic configuration of trauma care centers is key to maximizing accessibility while promoting the efficient use of resources. This research reports on the development of a two-stage stochastic optimization model for geospatial expansion of a trauma network in a delimited area. The stochastic optimization model recommends the siting of new trauma care centers according to the geographic distribution of the injured population. The model has the potential to benefit both patients and institutions, by facilitating prompt access and promoting the efficient use of resources. The findings indicate that the model significantly improves trauma care coverage, particularly in rural counties, thereby enhancing equitable access to critical healthcare services.

Long-term care facility networks in Canada face significant challenges in balancing demand and capacity, a problem exacerbated by rising demand. In other words, the growing elderly population is escalating the need for long-term care resources. To address this issue, this study proposes a Mixed-Integer Linear Programming model based on the current standing of the long-term care system in Ontario, a representative case for considering varied patient supports. The proposed model simultaneously optimizes the timing and location of constructing new long-term care facilities while dynamically adjusting each facility's capacity, including human resources and beds. Moreover, patient assignments are optimized based on their demand region, gender, language, and age group over a finite time horizon. The model incorporates multiple constraints to accommodate patients' gender and language, addressing language barriers, alleviating feelings of loneliness, and aligning with Canada's commitment to inclusive care. Additionally, it considers patient journeys by incorporating age groups and assigning patients from different demand regions in an equitable manner through the geographical equity constraint. To validate our proposed model, we conduct a case study on the existing network in Hamilton, Ontario. An extensive set of numerical analyses is executed to provide insights into the problem. Most importantly, the results demonstrate that the model effectively optimizes facility placement and patient allocation while significantly reducing un-assignment and misassignment rates. Specifically, the results indicate that over 88% of patient demand can be accommodated annually throughout a five-year planning horizon. In addition, patients can be assigned based on language and gender with marginal additional costs. Lastly, operational costs constitute the largest share of total expenditures, whereas misassignment costs account for the smallest proportion.