Health Care Management Science最新文献_第4页

Do adjustment costs constrain public healthcare providers' technical efficiency? Evidence from the New Zealand Public Healthcare System. 调整成本是否制约了公共医疗服务提供者的技术效率？来自新西兰公共医疗系统的证据。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-06-01 Epub Date: 2024-03-11 DOI: 10.1007/s10729-024-09668-5

Antony Andrews, Grigorios Emvalomatis

{"title":"Do adjustment costs constrain public healthcare providers' technical efficiency? Evidence from the New Zealand Public Healthcare System.","authors":"Antony Andrews, Grigorios Emvalomatis","doi":"10.1007/s10729-024-09668-5","DOIUrl":"10.1007/s10729-024-09668-5","url":null,"abstract":"Efficiency analysis is crucial in healthcare to optimise resource allocation and enhance patient outcomes. However, the prompt adaptation of inputs can be hindered by adjustment costs, which impact Long-Run Technical Efficiency (LRTE). To bridge this gap in healthcare literature, this research employs a Bayesian Dynamic Stochastic Frontier Model to estimate parameters and explore healthcare efficiency dynamics over time. The study reveals the LRTE for New Zealand District Health Boards (DHBs) as 0.76, indicating around 32% more input utilisation due to adjustment costs. Most DHBs exhibit consistent short-run operational efficiency, with the national Short-Run Technical Efficiency (SRTE) very close to the LRTE. Among the tertiary providers, Auckland and Capital & Coast DHBs operate below the LRTE level, setting them apart from other tertiary providers. Similarly, Tairawhiti and West Coast DHBs also fall below the LRTE level, as indicated by their SRTE scores, potentially influenced by their unique healthcare settings and resource challenges. This research brings a new perspective to policy discussions by incorporating the temporal dynamics of decision-making and considering adjustment costs. It underscores the need to balance short-term and long-term technical efficiency, underlining their collective significance in fostering a sustainable and efficient healthcare system in New Zealand.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"268-283"},"PeriodicalIF":2.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140101460","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}

引用次数: 0

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis. 病人路径中可解释预测的机器学习框架：预测有败血症症状的患者入住重症监护室的案例。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-06-01 Epub Date: 2024-05-21 DOI: 10.1007/s10729-024-09673-8

Sandra Zilker, Sven Weinzierl, Mathias Kraus, Patrick Zschech, Martin Matzner

{"title":"A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis.","authors":"Sandra Zilker, Sven Weinzierl, Mathias Kraus, Patrick Zschech, Martin Matzner","doi":"10.1007/s10729-024-09673-8","DOIUrl":"10.1007/s10729-024-09673-8","url":null,"abstract":"Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance - where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees - and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"136-167"},"PeriodicalIF":2.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11258202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141070883","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}

引用次数: 0

Efficiency effects of public hospital closures in the context of public hospital reform: a multistep efficiency analysis. 公立医院改革背景下关闭公立医院的效率效应：多步骤效率分析。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2024-03-01 Epub Date: 2023-12-06 DOI: 10.1007/s10729-023-09661-4

Songul Cinaroglu

{"title":"Efficiency effects of public hospital closures in the context of public hospital reform: a multistep efficiency analysis.","authors":"Songul Cinaroglu","doi":"10.1007/s10729-023-09661-4","DOIUrl":"10.1007/s10729-023-09661-4","url":null,"abstract":"In the wake of hospital reforms introduced in 2011 in Turkey, public hospitals were grouped into associations with joint management and some shared operational and administrative functions, similar in some ways to hospital trusts in the English National Health Service. Reorganization of public hospitals effect hospital and market area characteristics and existence of hospitals. The objective of this study is to examine the effect of closure on competitive hospital performances. Using administrative data from Turkish Public Hospital Statistical Yearbooks for the years 2005 to 2007 and 2014 to 2017, we conducted a three-step efficiency analysis by incorporating data envelopment analysis (DEA) and propensity score matching techniques, followed by a difference-in-differences (DiD) regression. First, we used bootstrapped DEA to calculate the efficiency scores of hospitals that were located near hospitals that had been closed. Second, we used nearest neighbour propensity score matching to form control groups and ensure that any differences between these and the intervention groups could be attributed to being near a hospital that had closed rather than differences in hospital and market area characteristics. Lastly, we employed DiD regression analysis to explore whether being near a closed hospital had an impact on the efficiency of the surviving hospitals while considering the effect of the 2011 hospital reform policies. To shed light on a potential time lag between hospital closure and changes in efficiency, we used various periods for comparison. Our results suggest that the efficiency of public hospitals in Turkey increased in hospitals that were located near hospitals that closed in Turkey from 2011. Hospital closure improves the efficiency of competitive hospitals under hospital market reforms. Future studies may wish to examine the efficiency effects of government and private sector collaboration on competition in the hospital market.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"88-113"},"PeriodicalIF":3.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138487394","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}

引用次数: 0

Scheduling mobile dental clinics: A heuristic approach considering fairness among school districts. 安排流动牙科诊所：考虑学区公平性的启发式方法。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2024-03-01 Epub Date: 2022-10-03 DOI: 10.1007/s10729-022-09612-5

Ignacio A Sepúlveda, Maichel M Aguayo, Rodrigo De la Fuente, Guillermo Latorre-Núñez, Carlos Obreque, Camila Vásquez Orrego

{"title":"Scheduling mobile dental clinics: A heuristic approach considering fairness among school districts.","authors":"Ignacio A Sepúlveda, Maichel M Aguayo, Rodrigo De la Fuente, Guillermo Latorre-Núñez, Carlos Obreque, Camila Vásquez Orrego","doi":"10.1007/s10729-022-09612-5","DOIUrl":"10.1007/s10729-022-09612-5","url":null,"abstract":"Mobile dental clinics (MDCs) are suitable solutions for servicing people living in rural and urban areas that require dental healthcare. MDCs can provide dental care to the most vulnerable high-school students. However, scheduling MDCs to visit patients is critical to developing efficient dental programs. Here, we study a mobile dental clinic scheduling problem that arises from the real-life logistics management challenge faced by a school-based mobile dental care program in Southern Chile. This problem involves scheduling MDCs to treat high-school students at public schools while considering a fairness constraint among districts. Schools are circumscribed into districts, and by program regulations, at least 50% of the students in each district must receive dental care during the first semester. Fairness prevents some districts from waiting more time to receive dental care than others. We model the problem as a parallel machine scheduling problem with sequence-dependent setup costs and batch due dates and propose a mathematical model and a genetic algorithm-based solution to solve the problem. Our computational results demonstrate the effectiveness of our approaches in obtaining near-optimal solutions. Finally, dental program managers can use the methodologies presented in this work to schedule mobile dental clinics and improve their operations.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"46-71"},"PeriodicalIF":3.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40394293","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}

引用次数: 0

The policy case for designating EMS teams for vulnerable patient populations: Evidence from an intervention in Boston. 为弱势病人群体指定急救队的政策依据：波士顿干预措施的证据。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2024-03-01 Epub Date: 2023-04-12 DOI: 10.1007/s10729-023-09635-6

Mark Brennan, Sophia Dyer, Jonas Jonasson, James Salvia, Laura Segal, Erin Serino, Justin Steil

{"title":"The policy case for designating EMS teams for vulnerable patient populations: Evidence from an intervention in Boston.","authors":"Mark Brennan, Sophia Dyer, Jonas Jonasson, James Salvia, Laura Segal, Erin Serino, Justin Steil","doi":"10.1007/s10729-023-09635-6","DOIUrl":"10.1007/s10729-023-09635-6","url":null,"abstract":"This study documents more than five years of analysis that drove the policy case, deployment, and retrospective evaluation for an innovative service model that enables Boston Emergency Medical Services (EMS) to respond quickly and effectively to investigation incidents in an area of heavy need in Boston. These investigation incidents are typically calls for service from passers-by or other third-party callers requesting that Boston EMS check in on individuals, often those who may appear to have an altered mental status or to be unhoused. First, this study reports the pre-intervention analytics in 2017 that built the policy case for service segmentation, a new Community Assistance Team designated \"Squad 80\" that primarily responds to investigation incidents in one broad area of the city with high rates of substance abuse and homelessness, helping patients who often refuse ambulance transport connect to social services. Second, this study reports a post-intervention, observational evaluation of its operational advantages and trade-offs. We observe that incidents involving the Community Assistance Team have significantly shorter response times and result in fewer transports to emergency departments than investigation incidents not involving the unit, leading to fewer ambulance unit-hours utilized across the system. This study documents the descriptive analytics that built the successful policy case for a substantive change in the healthcare-delivery supply chain in Boston and how this change offers operational advantages. It is written to be an accessible guide to the analysts and policy makers considering emergency services segmentation, an important frontier in equitable public-service delivery.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"72-87"},"PeriodicalIF":3.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10091313/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9299816","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}

引用次数: 0

Heterogeneous donor circles for fair liver transplant allocation. 异质捐献者圈，实现公平的肝移植分配。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-03-01 Epub Date: 2022-07-20 DOI: 10.1007/s10729-022-09602-7

Shubham Akshat, Sommer E Gentry, S Raghavan

{"title":"Heterogeneous donor circles for fair liver transplant allocation.","authors":"Shubham Akshat, Sommer E Gentry, S Raghavan","doi":"10.1007/s10729-022-09602-7","DOIUrl":"10.1007/s10729-022-09602-7","url":null,"abstract":"The United States (U.S.) Department of Health and Human Services is interested in increasing geographical equity in access to liver transplant. The geographical disparity in the U.S. is fundamentally an outcome of variation in the organ supply to patient demand (s/d) ratios across the country (which cannot be treated as a single unit due to its size). To design a fairer system, we develop a nonlinear integer programming model that allocates the organ supply in order to maximize the minimum s/d ratios across all transplant centers. We design circular donation regions that are able to address the issues raised in legal challenges to earlier organ distribution frameworks. This allows us to reformulate our model as a set-partitioning problem. Our policy can be viewed as a heterogeneous donor circle policy, where the integer program optimizes the radius of the circle around each donation location. Compared to the current policy, which has fixed radius circles around donation locations, the heterogeneous donor circle policy greatly improves both the worst s/d ratio and the range between the maximum and minimum s/d ratios. We found that with the fixed radius policy of 500 nautical miles (NM), the s/d ratio ranges from 0.37 to 0.84 at transplant centers, while with the heterogeneous circle policy capped at a maximum radius of 500 NM, the s/d ratio ranges from 0.55 to 0.60, closely matching the national s/d ratio average of 0.5983. Our model matches the supply and demand in a more equitable fashion than existing policies and has a significant potential to improve the liver transplantation landscape.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"20-45"},"PeriodicalIF":2.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10896798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40520364","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}

引用次数: 0

Accessible location of mobile labs for COVID-19 testing. COVID-19 检测流动实验室的便利位置。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2024-03-01 Epub Date: 2022-10-03 DOI: 10.1007/s10729-022-09614-3

Dianne Villicaña-Cervantes, Omar J Ibarra-Rojas

{"title":"Accessible location of mobile labs for COVID-19 testing.","authors":"Dianne Villicaña-Cervantes, Omar J Ibarra-Rojas","doi":"10.1007/s10729-022-09614-3","DOIUrl":"10.1007/s10729-022-09614-3","url":null,"abstract":"In this study, we address the problem of finding the best locations for mobile labs offering COVID-19 testing. We assume that people within known demand centroids have a degree of mobility, i.e., they can travel a reasonable distance, and mobile labs have a limited-and-variable service area. Thus, we define a location problem concerned with optimizing a measure representing the accessibility of service to its potential clients. In particular, we use the concepts of classical, gradual, and cooperative coverage to define a weighted sum of multiple accessibility indicators. We formulate our optimization problem via a mixed-integer linear program which is intractable by commercial solvers for large instances. In response, we designed a Biased Random-Key Genetic Algorithm to solve the defined problem; this is capable of obtaining high-quality feasible solutions over large numbers of instances in seconds. Moreover, we present insights derived from a case study into the locations of COVID-19 testing mobile laboratories in Nuevo Leon, Mexico. Our experimental results show that our optimization approach can be used as a diagnostic tool to determine the number of mobile labs needed to satisfy a set of demand centroids, assuming that users have reduced mobility due to the restrictions because of the pandemic.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"1-19"},"PeriodicalIF":3.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9527384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40394292","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}

引用次数: 0

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data. 利用非结构化文本数据预测急诊科患者24小时内的住院和等待时间。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2024-03-01 Epub Date: 2023-11-03 DOI: 10.1007/s10729-023-09660-5

Hyeram Seo, Imjin Ahn, Hansle Gwon, Hee Jun Kang, Yunha Kim, Ha Na Cho, Heejung Choi, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Dong-Woo Seo, Tae Joon Jun, Young-Hak Kim

{"title":"Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.","authors":"Hyeram Seo, Imjin Ahn, Hansle Gwon, Hee Jun Kang, Yunha Kim, Ha Na Cho, Heejung Choi, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Dong-Woo Seo, Tae Joon Jun, Young-Hak Kim","doi":"10.1007/s10729-023-09660-5","DOIUrl":"10.1007/s10729-023-09660-5","url":null,"abstract":"Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"114-129"},"PeriodicalIF":3.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10896961/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71434220","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}

引用次数: 0

Correction to: Prediction of hospitalization and waiting time within 24 h of emergency department patients with unstructured text data. 更正：利用非结构化文本数据预测急诊科患者的住院时间和 24 小时内的等待时间。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2024-03-01 DOI: 10.1007/s10729-023-09662-3

Hyeram Seo, Imjin Ahn, Hansle Gwon, Hee Jun Kang, Yunha Kim, Ha Na Cho, Heejung Choi, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Dong-Woo Seo, Tae Joon Jun, Young-Hak Kim

引用次数: 0

Editorial - Acknowledgement of reviewers and editorial board members. 编辑 - 感谢审稿人和编辑委员会成员。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2024-02-19 DOI: 10.1007/s10729-024-09666-7

引用次数: 0