Pub Date : 2026-03-23DOI: 10.1007/s10729-026-09758-6
Robert Messerle, Jonas Schreyögg
{"title":"Data-driven identification of outpatient-suitable procedures: a machine learning approach.","authors":"Robert Messerle, Jonas Schreyögg","doi":"10.1007/s10729-026-09758-6","DOIUrl":"https://doi.org/10.1007/s10729-026-09758-6","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147503715","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":"Optimization of diagnostic kits usage based on symptomatic presentation: an application of military decision making in public health.","authors":"Fumihiko Nakamura, Naoshi Tsuchida, Kazunori Matsuda, Takafumi Saikawa, Takashi Okumura","doi":"10.1007/s10729-026-09757-7","DOIUrl":"https://doi.org/10.1007/s10729-026-09757-7","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147467804","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}
Pub Date : 2026-03-14DOI: 10.1007/s10729-025-09752-4
Stefan Kempter, Jens O Brunner, Frank Hanses, Christoph Spinner, Lutz T Zabel, Christoph Römmele, Stefan Borgmann, Jörg Janne Vehreschild, Christina C Bartenschlager
{"title":"Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.","authors":"Stefan Kempter, Jens O Brunner, Frank Hanses, Christoph Spinner, Lutz T Zabel, Christoph Römmele, Stefan Borgmann, Jörg Janne Vehreschild, Christina C Bartenschlager","doi":"10.1007/s10729-025-09752-4","DOIUrl":"10.1007/s10729-025-09752-4","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12988995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147456772","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}
Pub Date : 2026-03-12DOI: 10.1007/s10729-026-09755-9
Erica Gralla, Nadia Lahrichi, Fannie Côté, Jade El Hage, Arunkumar Govindakarnavar, Victor J Del Rio Vilas
{"title":"Enhancing laboratory testing capacity: strategies for pandemic preparedness.","authors":"Erica Gralla, Nadia Lahrichi, Fannie Côté, Jade El Hage, Arunkumar Govindakarnavar, Victor J Del Rio Vilas","doi":"10.1007/s10729-026-09755-9","DOIUrl":"https://doi.org/10.1007/s10729-026-09755-9","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443492","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":"Strategic analysis of heterogeneous fleet composition for aerial interhospital transport in underserved areas of Canada.","authors":"Joelle Cormier, Valérie Bélanger, Marie-Éve Rancourt","doi":"10.1007/s10729-025-09748-0","DOIUrl":"https://doi.org/10.1007/s10729-025-09748-0","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147316659","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":"Identifying the most influential employees in infectious disease spread using stochastic mixed integer linear programming optimization.","authors":"Mohadese Basirati, Saeed Najafi-Zangeneh, Mireille Batton-Hubert","doi":"10.1007/s10729-025-09744-4","DOIUrl":"https://doi.org/10.1007/s10729-025-09744-4","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147316656","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}
Pub Date : 2026-02-21DOI: 10.1007/s10729-025-09750-6
Brad Beauvais, Zo Ramamonjiarivelo, C Scott Kruse, Lawrence Fulton, Ramalingam Shanmugam, Arvind Sharma, Aleksandar Tomic
In response to the growing number of hospital bankruptcies across the United States, this study sought to develop a predictive and interpretable model tailored specifically to the healthcare industry. Utilizing a longitudinal dataset of 3,091 short-term acute care hospitals from 2008 to 2021, we evaluated and compared traditional bankruptcy prediction models-Altman's Z'', Ohlson's O-score, and Zmijewski's model-against a newly developed hospital-specific logistic regression model (BRKFSST). We incorporated over 30 financial and hospital-level variables, including quality indicators, ownership type, and market characteristics. Unlike prior models, ours lagged all unknowable variables to ensure true out-of-sample prediction. The BRKFSST model achieved strong performance, with an Area Under the Curve (AUC) of 81.8%, balanced accuracy of 72.2%, and a mean recall of 60.6% across multiple test/train splits, outperforming all benchmark models. Importantly, the model retained interpretability, allowing for the identification of key predictors such as labor compensation ratio, adjusted patient days, and quality ratings. These findings provide actionable insights for hospital leaders and policymakers to identify at-risk institutions and implement early interventions to prevent financial collapse and preserve access to care.
{"title":"The predictive factors of US hospital bankruptcy - a multi-model comparison.","authors":"Brad Beauvais, Zo Ramamonjiarivelo, C Scott Kruse, Lawrence Fulton, Ramalingam Shanmugam, Arvind Sharma, Aleksandar Tomic","doi":"10.1007/s10729-025-09750-6","DOIUrl":"10.1007/s10729-025-09750-6","url":null,"abstract":"<p><p>In response to the growing number of hospital bankruptcies across the United States, this study sought to develop a predictive and interpretable model tailored specifically to the healthcare industry. Utilizing a longitudinal dataset of 3,091 short-term acute care hospitals from 2008 to 2021, we evaluated and compared traditional bankruptcy prediction models-Altman's Z'', Ohlson's O-score, and Zmijewski's model-against a newly developed hospital-specific logistic regression model (BRKFSST). We incorporated over 30 financial and hospital-level variables, including quality indicators, ownership type, and market characteristics. Unlike prior models, ours lagged all unknowable variables to ensure true out-of-sample prediction. The BRKFSST model achieved strong performance, with an Area Under the Curve (AUC) of 81.8%, balanced accuracy of 72.2%, and a mean recall of 60.6% across multiple test/train splits, outperforming all benchmark models. Importantly, the model retained interpretability, allowing for the identification of key predictors such as labor compensation ratio, adjusted patient days, and quality ratings. These findings provide actionable insights for hospital leaders and policymakers to identify at-risk institutions and implement early interventions to prevent financial collapse and preserve access to care.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12924785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146258017","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}
Pub Date : 2026-02-19DOI: 10.1007/s10729-025-09751-5
Vrishabh Patil, Kara K Hoppe, Yonatan Mintz
A key challenge in medical decision making is learning treatment policies for patients with limited observational data. This challenge is particularly evident in personalized healthcare decision-making, where models need to take into account the intricate relationships between patient characteristics, treatment options, and health outcomes. To address this, we introduce prescriptive neural networks (PNNs), shallow 0-1 neural networks trained with mixed integer programming that can be used with counterfactual estimation to optimize policies in medium data settings. These models offer greater interpretability than deep neural networks and can encode more complex policies than common models such as decision trees. We show that PNNs can outperform existing methods in both synthetic data experiments and in a case study of assigning treatments for postpartum hypertension. In particular, PNNs are shown to produce policies that could reduce peak blood pressure by 5.47 mm Hg (p=0.02) over existing clinical practice, and by 2 mm Hg (p=0.01) over the next best prescriptive modeling technique. Moreover PNNs were more likely than all other models to correctly identify clinically significant features while existing models relied on potentially dangerous features such as patient insurance information and race that could lead to bias in treatment.
{"title":"Healthcare applications of 0-1 neural networks in prescriptive problems with observational data.","authors":"Vrishabh Patil, Kara K Hoppe, Yonatan Mintz","doi":"10.1007/s10729-025-09751-5","DOIUrl":"10.1007/s10729-025-09751-5","url":null,"abstract":"<p><p>A key challenge in medical decision making is learning treatment policies for patients with limited observational data. This challenge is particularly evident in personalized healthcare decision-making, where models need to take into account the intricate relationships between patient characteristics, treatment options, and health outcomes. To address this, we introduce prescriptive neural networks (PNNs), shallow 0-1 neural networks trained with mixed integer programming that can be used with counterfactual estimation to optimize policies in medium data settings. These models offer greater interpretability than deep neural networks and can encode more complex policies than common models such as decision trees. We show that PNNs can outperform existing methods in both synthetic data experiments and in a case study of assigning treatments for postpartum hypertension. In particular, PNNs are shown to produce policies that could reduce peak blood pressure by 5.47 mm Hg (p=0.02) over existing clinical practice, and by 2 mm Hg (p=0.01) over the next best prescriptive modeling technique. Moreover PNNs were more likely than all other models to correctly identify clinically significant features while existing models relied on potentially dangerous features such as patient insurance information and race that could lead to bias in treatment.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":"6"},"PeriodicalIF":2.0,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12920400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146226593","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}
Pub Date : 2026-02-19DOI: 10.1007/s10729-025-09743-5
Hayo Bos, Gaspard Hosteins, Wick Wijnholds, Aleida Braaksma, Gréanne Leeftink
{"title":"Optimization of tray inventory levels in hospitals from an integral perspective.","authors":"Hayo Bos, Gaspard Hosteins, Wick Wijnholds, Aleida Braaksma, Gréanne Leeftink","doi":"10.1007/s10729-025-09743-5","DOIUrl":"10.1007/s10729-025-09743-5","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":"7"},"PeriodicalIF":2.0,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12920321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146226526","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}
Pub Date : 2026-02-12DOI: 10.1007/s10729-025-09749-z
Zequn Chen, Wesley J Marrero
{"title":"A survey on optimization and machine learning-based fair decision making in healthcare.","authors":"Zequn Chen, Wesley J Marrero","doi":"10.1007/s10729-025-09749-z","DOIUrl":"https://doi.org/10.1007/s10729-025-09749-z","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"29 1","pages":"5"},"PeriodicalIF":2.0,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146165083","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}
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