Health Care Management Science最新文献

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.

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.

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.

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.

The diagnosis section in hospital discharge summaries plays a critical role in ensuring continuity of care by providing essential diagnostic information and a succinct summary of a patient's condition to subsequent caregivers. However, the lack of standardized structure and content can lead to incomplete, ambiguous, or inaccurate documentation, potentially compromising patient safety. This study takes a foundational step toward standardizing the diagnosis section in German, and potentially international, discharge summaries by developing a taxonomy of structural and content elements and examining the use of standardized terminologies and abbreviations. We conducted a retrospective analysis of 436 de-identified discharge summaries from 112 hospitals across 12 German states. A structured taxonomy development process was applied, supported by natural language processing, to examine structural and content elements as well as the use of standardized terminologies (SNOMED-CT, ICD-10 codes) and abbreviations. The resulting taxonomy for diagnosis sections comprises 87 distinct characteristics across three meta-dimensions: structure, content, and levels of detail. The analysis revealed limited adoption of standardized terminologies; only 8.1% of terms conformed to SNOMED-CT, and only 14.2% of diagnosis sections included ICD-10 codes. Abbreviations appeared in 92% of diagnosis sections, constituting 14.5% of all words, many of which were obscure or infrequently used. These findings underscore the urgent need for a standardized, interoperable, and clinically meaningful diagnosis section to support continuity of care and data-driven healthcare. The proposed taxonomy offers a foundational framework for future standardization efforts by providing structural and content "design options."

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.

In positive and unlabeled (PU) learning problems, only positive examples are labeled. Unlabeled data contain both positive and negative examples. Studies show that positive examples of (secondary) diagnoses, and clinical conditions, such as sepsis, are present in unlabeled hospital administrative data, potentially distorting hospital reimbursement systems, and negatively affecting hospitals' revenue and profitability. We investigate whether PU learning is suitable for improving the quality of hospital administrative data. We train three models on 313,434 hospital cases using hospital cost features: two based on the two-step "spy" approach and one using a robust PU learning method. For model evaluation, we rely exclusively on positive examples due to the PU setting. To further assess model performance, we perform an external validity check: We relabel unlabeled sepsis cases, derive new sepsis rates, and compare them to those reported in medical record review studies. All models identify true positives well in unseen data. External validity checks show, however, that only the robust PU learner effectively discriminates between positives and negatives in the unlabeled data, yielding new sepsis rates within the range of sepsis rates reported in medical record review studies. PU learning can improve the quality of hospital administrative data, but its effectiveness depends strongly on the choice of learning approach and classifier. The output of a PU learner can potentially improve hospital reimbursement systems, hospital revenue and profitability management, and sensitivity analyses in healthcare management science, health economics, health services research, and disease surveillance.

The COVID-19 pandemic shed light on the fragility of today's public health systems and failure to sufficiently invest in preparedness. These shortcomings are observed in delays achieving timely, equitable, and sufficient access to life-saving vaccines when faced with erratic demand. This Current Opinion describes vaccine supply networks (VSNs) from a complex adaptive systems (CAS) lens, highlighting interactions between system elements and co-evolution with the environment in which they operate. More specifically, it shows how broadening the boundaries of VSNs reveals the high degree of complexity that leads to unexpected and emergent system behavior, especially when disease threats evolve over time and across geographies. A CAS lens allows for the design of improved management strategies to ensure continued performance of VSNs during both outbreak and inter-epidemic periods, thus contributing to sustained disease management. It points to ample opportunities for more integrated modeling across disciplines to capture inherent feedback loops that influence both VSNs and disease dynamics. Furthermore, it reveals how pandemic preparedness relies on a broader understanding of the mechanisms that drive outbreak prevention and control, beyond vaccines and their direct supply chains. Finally, it highlights the value of adaptive management to navigate inevitable future disruptions and associated uncertainties, overcoming limitations of typical risk-mitigation strategies based on prediction and control.