Health Care Management Science最新文献

This current opinion explores the transformative potential of explainable artificial intelligence (XAI) for health care management systems. While AI has already demonstrated substantial benefits in clinical decision-making, operational efficiency and patient outcomes, its adoption is often hindered by the lack of transparency in AI-driven decision-making. XAI bridges this gap by providing interpretability, thereby increasing trust between policy-makers, clinicians, administrators and patients. However, despite promising examples, the explicit integration of XAI remains underexplored in health care management research. This current opinion therefore aims to emphasize the crucial role of XAI in improving health care management and to position it as an important topic for advancing the field, with Health Care Management Science (HCMS) playing a leadership role in fostering this development.

The ability to accurately forecast unscheduled care needs is of paramount importance for decision making in healthcare operations, ensuring a continuous and high-quality level of care. In this work, we provide a literature review of 156 research articles of forecasting applications with special focus on care services that are not scheduled in advance such as emergency departments. Our paper presents two key contributions. Firstly, we propose a novel framework designed to characterize the application of forecasting process across various unplanned healthcare services. Our taxonomy facilitates the detection, decomposition, and categorization of forecasting processes, enhancing the understanding of their deployment in different unscheduled care settings. Secondly, we conduct a comprehensive literature review based on a systematic search, critically analyzing the state of forecasting research in unscheduled care services and identifying key research gaps. We explore forecasting problems in depth, examining their purpose, the various methodologies used, the rigor used in generating and evaluating forecasts, and the reproducibility of results, all within the context of the proposed framework. By consolidating the current state of the art, this paper provides valuable insights to both healthcare professionals and academics regarding the effective application of forecasting in unscheduled care services. Finally, it serves as a roadmap for identifying major research gaps and outlines an agenda for future investigations.

Ensuring widespread accessibility of healthcare services is a crucial policy objective. Accordingly, the Italian National Recovery and Resilience Plan (NRRP) has prioritized territorial medicine, channeling post-pandemic investments toward the restructuring of primary care services. A notable change is the establishment of Community Healthcare Centers (CHCs). This paper investigates how CHCs contribute to the accessibility of healthcare in urban and rural areas. By leveraging a comprehensive dataset of general practitioners' availability and estimating future demand-and-supply scenarios, we examine the impact of CHCs under two different capacity allocation strategies. Strategy 1-Capacity expansion-involves allocating additional service hours of general practitioners to CHCs in order to maximize accessibility. Strategy 2-Capacity redistribution-accounts for the persistent shortage of healthcare professionals faced by Italy in the recent years by reallocating a portion of general practitioners' current services from their existing workplace locations to CHCs. Our results indicate that CHCs have the potential to maintain current accessibility levels and also enhance them in the years to come. Moreover, we demonstrate that simply redistributing the current capacity can improve future accessibility. Finally, we show that a mix of the capacity expansion and redistribution strategies (Strategy 3) can maximize accessibility in the future, limiting the need for new professional staff.

In this research, we present an interpretable AutoML approach for the early diagnosis of hypertension and hyperinsulinemia among adolescents, conditions that are critical to identify during these formative years due to their requirement for lifelong care and monitoring. The dataset, collected from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, posed challenges common to medical datasets, including imbalances, data scarcity, and a need for transparent, explainable predictive models. To counter these issues, we utilized three AutoML frameworks - AutoGluon, H2O, and MLJAR - in conjunction with a Tabular Variational Autoencoder (TVAE) to synthetically augment the data points, Prinicipal Component Analysis (PCA) for dimensionality reduction, and SHapley Additive exPlanations (SHAP) and Permutation feature importance analyses to extract insights from the results. AutoGluon outperformed the others on the original dataset, delivering better results with weighted ensemble models for both conditions under a 12-minute budget-time constraint and maintaining all evaluation metrics below a 4% threshold, all without the need for further scaling or calibration in the experimental setup. Our research underscores the broad applicability of the current AutoML paradigm, highlighting its particular benefits for the healthcare domain and diagnostics, where such advanced tools can enhance patient care.

This work proposes a two-phase approach for optimizing the layout of a referral cancer center in Latin America, which results from a collaboration between a university, in Brazil, and a consultancy company. The objective of the problem is to minimize the total transportation cost of patients, medical/non-medical staff, materials, and equipment. In the first phase, an integer programming model is used to assign departments to floors in such a way that the vertical transportation cost between departments is reduced. In the second phase, a heuristic method is employed to determine the layout of the blocks across a given floor, when applicable, while minimizing the transportation cost within the floor. Process mining is employed to gather data associated with the movement flow between rooms and departments within the unit. The developed approach was used by the managers of the cancer center to help design a new hospital building, and the layout produced by the optimization procedure was compared with the initial layout originally built by the architects of the hospital. The results obtained demonstrate that our method was capable of significantly reducing both vertical ( $-19.7\%$ ) and horizontal ( $-22.7\%$ ) transportation costs within the hospital.

Life expectancy is typically shorter in areas with higher deprivation, highlighting the need for policymakers and health care managers to focus on reducing health inequalities through efficient and effective care. This study aims to assess the impact of deprivation on primary health care performance using data from the National Health Service (NHS) in England. Two methods are applied: Data Envelopment Analysis (DEA) to evaluate the performance of 188 Clinical Commissioning Groups (CCGs), whose duties were recently taken on by the new Integrated Care Systems (ICSs), and the Malmquist Index (MI) to assess deprivation's effect on performance. The DEA results reveal significant variation among CCGs in equity, efficiency, and effectiveness, indicating substantial room for improvement. The MI results show that while CCGs in more deprived areas had more resources per capita and higher efficiency, they were generally less effective than those in less deprived areas. This emphasizes the need to enhance health and social policies to address persistent health inequalities due to deprivation, a critical challenge for the new ICSs. This study illustrates how DEA and the MI can support policymakers and managers in this effort.

Ambulatory care facilities play a critical role in many healthcare systems worldwide. To ensure efficient care provision, we must match care demand with care supply. To support provider decision-making, this article reviews Operations Research planning problems, the corresponding planning and control decisions that must be made when opening up or running an ambulatory care facility, and their data requirements. We give an overview of demand and supply-related data that an ambulatory care facility can collect and comment on the consequences for decision-making if some of that data is missing. We briefly discuss three healthcare systems and their influence on data collection and decision-making. We also take a closer look at several real-world appointment data sets and their usefulness for planning decisions. In addition, we discuss model implementation barriers and give recommendations for modelers and practitioners to bridge the gap between theory and practice. Finally, we present future research directions for Operations Research in ambulatory care.

Cardiovascular diseases (CVDs) are one of the primary reasons for death worldwide. These diseases often occur due to the occlusion of coronary arteries, thereby leading to insufficient blood and oxygen supply that damages cardiac muscle cells. Electrocardiogram (ECG) signals which reflect heart electrical activity are being used for diagnosing various cardiac diseases. Typically, a standard ECG consists of 12 channels referred to as leads which enable practitioners to monitor heartbeats through different channels where each heartbeat lasts approximately 600 ms. The majority of studies focus on the classification and early diagnosis of arrhythmias. Although the current studies on change-point methods have acquired massive accuracy in detecting potential shifts during a multi-channel process, they lack flexibility in manually assigning more weights to the channels, which are of more importance for experts. This could be addressed by implementing the weighted multivariate functional principal component analysis (WMFPCA). The objective of this study is to develop a novel change-point detection method to monitor long-term cardiovascular treatment. A third-order tensor structure was employed to represent the 12-lead ECG data in three dimensions (beats × samples × leads). Exploiting intra-beat, inter-beat, and inter-lead correlations along with channel significance in the third-order tensor, the WMFPCA is incorporated into Hotelling's $T^2$ statistic to construct monitoring schemes. Simulation results show that the proposed approach outperforms the existing methods in monitoring multi-channel processes. Finally, applying the suggested model on a real-world dataset containing Myocardial Infarction (MI) subjects verifies the model.

Multi-billion-dollar opioid settlement agreements have been reached with pharmaceutical manufacturers and distributors to address their liability in contributing to the opioid epidemic in the United States. These agreements stipulate that within the state, the settlement funds must be directly allocated to local government (e.g., counties) and used for abatement activities to remediate the harm of the opioid epidemic in communities. This naturally leads to an important question of how the funds should be distributed to meet the diverse needs of the counties consistently across all counties to be deemed fair. Although there exist various definitions of fairness in the literature, it remains unclear how to empirically quantify the fairness of settlement allocation based on data, which is crucial for developing evidence-based allocation policies. To fill this gap, we define two allocation fairness measures, deviation and maximum regret, and formulate the fair settlement allocation as convex optimization problems. To further enhance the interpretability of the allocation policies, we restrict the allocation to a weighted sum of the given empirical metrics. We apply our analytical framework in a case study of the settlement allocation in Pennsylvania using real-world empirical metrics. We identify the frontiers of the non-dominated allocation policies between min-deviation and minimax-regret allocations, which dominate all alpha fairness-based and formula-based allocation policies. All allocation policies show lower fairness (with higher deviation or maximum regret) in counties that are rural, low-income, and with lower-ranking health factors. The price of interpretability is more significant in terms of maximum regret compared with deviation.

With the advancement in computing power and data science techniques, reinforcement learning (RL) has emerged as a powerful tool for decision-making problems in complex systems. In recent years, the research on RL for healthcare operations has grown rapidly. Especially during the COVID-19 pandemic, RL has played a critical role in optimizing decisions with greater degrees of uncertainty. RL for healthcare applications has been an exciting topic across multiple disciplines, including operations research, operations management, healthcare systems engineering, and data science. This review paper first provides a tutorial on the overall framework of RL, including its key components, training models, and approximators. Then, we present the recent advances of RL in the domain of healthcare operations management (HOM) and analyze the current trends. Our paper concludes by presenting existing challenges and future directions for RL in HOM.