Health Care Management Science最新文献

{"title":"Optimizing vaccine site locations while considering travel inconvenience and public health outcomes.","authors":"Suyanpeng Zhang, Sze-Chuan Suen, Han Yu, Maged Dessouky, Fernando Ordonez","doi":"10.1007/s10729-026-09778-2","DOIUrl":"10.1007/s10729-026-09778-2","url":null,"abstract":"<p><p>During the COVID-19 pandemic, selecting vaccination sites and allocating limited doses required balancing accessibility, disease control, and fairness. We formulate a multi-objective mixed-integer linear programming model that jointly determines the locations of mega-sites and allocates vaccine doses while explicitly incorporating travel inconvenience, disease dynamics, and equitable distribution. The model incorporates commuting patterns from both residential and workplace origins to more accurately capture population mobility, and employs a tractable objective formulation that proxies key public health goals, enabling efficient and equitable mass vaccination planning. Compared with the solution empirically used in Los Angeles County in 2020, we recommend more dispersed mega-site locations that result in a 26% reduction in travel inconvenience and avert an additional 200 infections.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13364939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An online algorithm for integrated scheduling of pre-treatment and treatment appointments in radiotherapy using deep reinforcement learning.","authors":"Maxim Frankish, Broos Maenhout","doi":"10.1007/s10729-026-09762-w","DOIUrl":"10.1007/s10729-026-09762-w","url":null,"abstract":"<p><p>Scheduling patients for radiotherapy within the oncology department is vital for ensuring timely and effective treatment. The patient's therapy consists of a sequence of pre-treatment consultations, i.e. preliminary investigations to define the required treatment, followed by the radiotherapy treatment sessions on linear accelerators. Timely scheduling of pre-treatment consultations is crucial to prevent unnecessary delays in radiotherapy treatment, thereby impacting patient health. Therefore, we suggest integrating both scheduling problems. We employ a Reinforcement Learning-based scheduling technique to solve the multi-appointment scheduling problem in an online fashion. We design a reward function comprising three components to guide the agent towards learning an effective scheduling policy involving multiple appointments. Additionally, we compare our proposed model with benchmark online heuristics. We further provide insight into the reward function design, which steers the agent to learn a feasible policy that leads to high-quality solutions. Experiments show that our algorithm outperforms the benchmark heuristics.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148420682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal hospital capacity management during demand surges.","authors":"Felix Parker, Fardin Ganjkhanloo, Diego A Martínez, Kimia Ghobadi","doi":"10.1007/s10729-026-09764-8","DOIUrl":"10.1007/s10729-026-09764-8","url":null,"abstract":"<p><p>Effective hospital capacity management is critical for enhancing patient care quality, operational efficiency, and healthcare system resilience, notably during demand spikes like those seen in the COVID-19 pandemic. However, devising optimal capacity strategies is complicated by fluctuating demand, conflicting objectives, and multifaceted practical constraints. This study presents a data-driven framework to optimize capacity management decisions within hospital systems during surge events. Two key decisions are optimized over a tactical planning horizon: allocating dedicated capacity to surge patients and transferring incoming patients between emergency departments (EDs) of hospitals to better distribute demand. The optimization models are formulated as robust mixed-integer linear programs, enabling efficient computation of optimal decisions that are robust against demand uncertainty. The models incorporate practical constraints and costs, including setup times and costs for adding surge capacity, restrictions on ED patient transfers, and relative costs of different decisions that reflect impacts on care quality and operational efficiency. The methodology is evaluated retrospectively in a hospital system during the height of the COVID-19 pandemic to demonstrate the potential impact of the recommended decisions. The results show that optimally allocating beds and transferring just 32 patients over a 63 day period around the peak, about one transfer every two days, could have reduced the need for surge capacity in the hospital system by nearly 90%. Overall, this work introduces a practical tool to transform capacity management decision-making, enabling proactive planning and the use of data-driven recommendations to improve outcomes.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148307883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards optimal valve prescription for transcatheter aortic valve replacement (TAVR) surgery: a machine learning approach.","authors":"Phevos Paschalidis, Vasiliki Stoumpou, Lisa Everest, Yu Ma, Talhat Azemi, Jawad Haider, Steven Zweibel, Eleftherios M Protopapas, Jeff Mather, Maciej Tysarowski, George E Sarris, Robert C Hagberg, Howard L Haronian, Dimitris Bertsimas","doi":"10.1007/s10729-026-09768-4","DOIUrl":"10.1007/s10729-026-09768-4","url":null,"abstract":"<p><p>Transcatheter Aortic Valve Replacement (TAVR) has emerged as a prominent, minimally invasive treatment for patients with severe aortic stenosis, a life-threatening cardiovascular condition. Multiple transcatheter heart valves (THV) have been approved for use in TAVR, but current guidelines regarding valve type prescription remain a topic of ongoing debate within the medical community. We propose a data-driven clinical support tool to identify the optimal valve type with the objective of minimizing the risk of permanent pacemaker implantation (PPI), a predominant postoperative complication. We synthesize a novel dataset, combining U.S. and Greek patient populations, that integrates data from three distinct sources (patient demographics, computed tomography scans, echocardiograms) while harmonizing the different encoding processes specific to each country's record system. We propose leaf-level analysis to leverage the heterogeneity of the patient populations and avoid benchmarking against uncertain counterfactual risk estimates. The final prescriptive model shows a reduction in PPI rates of 26% and 16% compared to the current standard of care in our internal U.S. population and external, Greek validation set, respectively. To the best of our knowledge, this work represents the first unified, personalized prescription strategy for THV selection in TAVR.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148294868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient engagement: opportunities for mobile methadone maintenance in geographically underserved areas.","authors":"Anthony Bonifonte, Erin Garcia, Sean Soth, Hang Ngo","doi":"10.1007/s10729-026-09773-7","DOIUrl":"10.1007/s10729-026-09773-7","url":null,"abstract":"<p><p>In 2021 the Drug Enforcement Agency changed regulations to reduce barriers in the registration and operation of mobile methadone clinics for treating opioid use disorder in rural and underserved areas. How these routes might best be operated and how many clients might be served by mobile units are open questions. This work identifies candidate dispensing locations from mobile units in areas that do not have geographic access to a fixed clinic. Optimal routes for mobile methadone are identified by solving a mixed integer program that maximizes the number of clients without access to care that can be served by a mobile route. Ohio is used as a case study state to explore the efficiency of routes leaving from each methadone clinic in the state. The approach is generalized nationwide, and multi-vehicle routes are analyzed. Between 48 and 68 clients can be served by a single vehicle in a day (mean of 60) under default parameter assumptions across Ohio. Similarly, nationwide routes serve a mean of 47.5 clients per day. The second and third vehicle routes from a clinic provide access to an additional 95% and 93% of the clients of single vehicle routes, respectively. The number of servable clients on any route greatly differs based on geography. This work demonstrates the potential value of mobile methadone maintenance in reaching populations without geographic access to a fixed clinic. Additional policy and funding changes are needed to incentivize the operation of mobile methadone units for serving rural and unmet demand.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13275589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148264255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ahead of the ambulance: Optimizing volunteer training.","authors":"Brian Overbeek, Pieter L van den Berg, Caroline J Jagtenberg, Rob van der Mei","doi":"10.1007/s10729-026-09771-9","DOIUrl":"10.1007/s10729-026-09771-9","url":null,"abstract":"<p><p>Emergency response for medical incidents is increasingly extended by community first responder (CFR) systems that dispatch nearby trained volunteers. The implementation of CFR systems has led to significant decreases in emergency response times, especially in rural areas where ambulances take longer to arrive. CFR systems that dispatch volunteers to various emergency types can increase their effectiveness by training their volunteers, enabling these volunteers to provide first aid for more emergency types. We study the problem of optimizing a CFR system's training strategy to maximize its effectiveness given a limited budget, where the effectiveness is measured by the probability that at least one volunteer arrives before the ambulance for any given incident. We introduce an optimization model that explicitly accounts for the heterogeneous nature of volunteers' availability and locations, as well as a solution approach that efficiently obtains optimal solutions for realistically-sized instances. We apply the optimization approach to a CFR system operating in Lincolnshire, United Kingdom. The results show that the optimization approach yields substantially larger improvements in the CFR system's effectiveness compared to several intuitive greedy training strategies. Additionally, dispatch restrictions to limit the workload of volunteers are shown to have important implications for the optimal training strategy.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13253763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel multi-criteria doctor ranking method considering patient risk attitudes and compensatory relationships.","authors":"Xihua Li, Fengyu Zhang, Jiayi Chen","doi":"10.1007/s10729-026-09776-4","DOIUrl":"10.1007/s10729-026-09776-4","url":null,"abstract":"<p><p>In the post-pandemic era, the demand for Internet-based healthcare services has soared, with an increasing number of patients turning to online consultations. However, the overwhelming volume of information on online healthcare platforms often makes it challenging for patients to quickly identify the most suitable doctors, and uniform recommendation lists provided by these platforms fail to consider patients' different risk attitudes. These challenges highlight the need for effective decision-support tools to help patients navigate the complex landscape of online consultations. This paper proposes a novel multi-criteria decision-making approach that incorporates patients' risk attitudes and compensatory relationships among criteria. The method employs reference-dependent utility functions to model patients' varying risk attitudes towards gains and losses. It also captures and expresses the ambiguity and hesitation inherent in online reviews through a proposed word representation model, while accounting for compensatory relationships among criteria. A case study on Haodf platform demonstrates the potential of the proposed method to help patients make informed decisions when selecting healthcare providers. Comparative and sensitivity analyses further highlight its practicality and effectiveness. This study provides valuable insights for healthcare providers and platform operators in the rapidly growing field of online medical consultations.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions.","authors":"Lasse Bohlen, Julian Rosenberger, Nico Hambauer, Daniel Zähringer, Volkmar Franz, Patrick Zschech, Mathias Kraus","doi":"10.1007/s10729-026-09760-y","DOIUrl":"10.1007/s10729-026-09760-y","url":null,"abstract":"<p><p>The application of machine learning (ML) models in healthcare management offers high potential. In particular, resource allocation and operational decision-making in intensive care units (ICUs) can benefit from ML predictions, leading to improvements in patient outcomes and operational efficiency. However, the generalizability of these models across diverse hospital settings with potentially different patient populations remains a critical challenge. This study examines the generalizability of ML-based ICU outcome prediction models built using external data. We utilize data from two sources: a European University Hospital (EUH) dataset from Universitätsklinikum Carl Gustav Carus Dresden, Germany and the Medical Information Mart for Intensive Care (MIMIC)-IV database, representing different healthcare systems and patient populations. Our approach evaluates multiple models of varying architectures and complexity across three common prediction tasks in ICU settings (mortality, length of stay, and readmission), analyzes the impact of data availability on model performance, and applies interpretability techniques to identify features and scenarios where models succeed or fail in new environments. We found that locally trained models generally outperform those using external data when sufficient local data is available. Low and medium complexity models, such as generalized additive models, demonstrate significantly superior generalizability compared to high complexity models and require substantially less local data for high-quality predictions, offering evidence-based guidance for healthcare managers dealing with limited data resources. Our results demonstrate how interpretability techniques can identify dataset differences that hinder generalizability, providing valuable insights for healthcare practitioners in implementing ML solutions across diverse hospitals. This research contributes to the development of more generalizable and interpretable ML models in healthcare.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13253713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic outpatient sequencing on single examination equipment considering late arrivals.","authors":"Jiamou Su, Huili Guo, Junpeng Wang, Lihui Zeng, Yang Wang, Li Luo","doi":"10.1007/s10729-026-09770-w","DOIUrl":"10.1007/s10729-026-09770-w","url":null,"abstract":"<p><p>This study addresses the dynamic outpatient sequencing (DPS) problem on a single medical examination equipment considering late arrivals to reduce the weighted waiting time of outpatients. Our DPS problem distinguishes punctual patients and late patients by giving them different weights of waiting time to favor the on-time arrivals. To handle this problem, we propose a genetic programming (GP) algorithm combined with feature selection and niching technique to tackle the stochastic factors such as outpatient tardiness and examination duration. The feature selection helps to improve the learning performance and the interpretability of the generated dispatching rule. The niching technique is able to maintain a good population diversity to prevent the search premature convergence. Our GP algorithm generates a combined dispatching rule (CDR-GP) that dynamically selects the next outpatient to take examination based on an integrated measure of the number of examination items, late arrival time, waiting time and appointment time. We generate different scenarios to simulate patient tardiness and medical examination resource availability and compare CDR-GP with widely used dispatching rules. The experimental results demonstrate that CDR-GP consistently outperforms all benchmark methods, achieving improvements ranging from 0.04% to 50.69% across all scenarios. Moreover, a case study conducted at a 3A hospital in China reveals that, under comparable parameter conditions, CDR-GP reduces outpatient waiting times by 38.20% compared to real-world practices.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148137817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning to dispatch volunteers to out-of-hospital cardiac arrests.","authors":"Pieter L van den Berg, Océane Fourmentraux, Shane G Henderson, Caroline J Jagtenberg, Hemeng Li","doi":"10.1007/s10729-026-09763-9","DOIUrl":"10.1007/s10729-026-09763-9","url":null,"abstract":"<p><p>Survival for out-of-hospital cardiac arrest can be significantly improved through volunteer efforts. To shorten the time to good-quality cardiopulmonary resuscitation, some emergency call centers use mobile phone technology to rapidly locate and alert nearby trained volunteers. Some such community first responder systems use phased alerts: notifying increasingly many volunteers with built-in time delays. The policy that defines the phasing of alerts affects both response times, which have a direct relation to survival, and the burden on volunteers. We aim to optimize this policy, which involves trading off these two metrics. The policy may depend on real-time information: where the volunteers are observed in relation to the patient and how long triage took. A direct approach using dynamic programming yields some insights, but is too slow for real-time use. Our contribution lies in recasting this problem as a multi-class classification problem and solving it using empirical data from Auckland, New Zealand's community first response system. This case study shows that phasing the alerts based on real-time information provides important improvements relative to a competitive baseline that is indicative of current practice.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13219187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148042044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}