Health Care Management Science最新文献

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 30 days. 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.

Contagious diseases severely impact health systems and economies, with close contact leading to further spread and fatalities. This paper examines the effects of government interventions on controlling such diseases. Key interventions include media isolation of susceptible individuals, effective quarantining of infected persons, and vaccination. A system dynamics approach models the complexities of government interventions in coronary conditions. We used the SEIR (Susceptible, Exposed, Infected, and Recovered) model and developed a new model to address its shortcomings for a new virus. Resilience actions were defined and plotted based on the emergency management cycle phases: Prevention, Preparedness, Response, and Recovery. The model can be applied to any contagious disease worldwide. We calibrated the model using data from sources like the World Health Organization (WHO) and Centers for Disease Control (CDC), and validated it against official and historical data. A sensitivity analysis was conducted based on various resilience strategies: Isolation Rate Slope, Isolation Efficiency, Minimum Isolation Rate, Quarantine Portion, Quarantine Transmission, Vaccination Rate, and Media Rate Slope. The study identifies key conditions for controlling outbreaks: achieving rapid isolation with a minimum rate above 50% and efficiency above 95%, rapid detection and quarantine above 90% with efficiency over 92%, and an optimal contact rate below 0.2, achieved with a media rate slope of 0.005 and vaccination rate above 90%. These measures can control the disease within 455 days or less.

In the context of diagnosis of COVID-19, this paper shows how to convert a Causal Network to a Large Language Model (LLM). The Causal Network was converted to the language model using prompts and completions. Prompts were composed from the full-factorial combination of the text associated with statistically significant variables in the Causal Network. Completions were based on the evaluation of the probability of COVID-19 using the Causal Network. The accuracy of the Causal Network and LLM was tested using two databases. The first database was based on a survey of 822 patients, collecting 12 direct (parents on the Markov blanket of COVID-19 diagnosis node), 7 indirect (associated with COVID-19 but not direct cause) symptoms of COVID-19. The second set was based on 80 patients reporting their symptoms in open-ended questions, often reporting some of the direct predictors and rarely reporting any indirect predictors of COVID-19. The accuracy of Causal Network and Markov blanket was tested using Area under the Receiver Operating Curve (AUROC). When indirect information was available, the Causal Network model (AUROC = 0.91) was significantly more accurate than the LLM (AUROC = 0.88), even though LLM model was trained to duplicate predictions of the Causal Network. Where the indirect information was not available, both models had lower accuracy (AUROC of 0.75 and 0.76). The accuracy of LLM depends not only on patterns among direct predictors of the outcome but also data not reported to the LLM. Conversational LLMs need to go beyond information the patient supplies and proactively ask about missing, typically indirect, information.

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.

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."