Health Care Management Science最新文献_第5页

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

Select, route and schedule: optimizing community paramedicine service delivery with mandatory visits and patient prioritization. 选择、路线和时间表：通过强制就诊和患者优先顺序优化社区护理服务提供。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-07-18 DOI: 10.1007/s10729-023-09646-3

Shima Azizi, Özge Aygül, Brenton Faber, Sharon Johnson, Renata Konrad, Andrew C Trapp

{"title":"Select, route and schedule: optimizing community paramedicine service delivery with mandatory visits and patient prioritization.","authors":"Shima Azizi, Özge Aygül, Brenton Faber, Sharon Johnson, Renata Konrad, Andrew C Trapp","doi":"10.1007/s10729-023-09646-3","DOIUrl":"10.1007/s10729-023-09646-3","url":null,"abstract":"Healthcare delivery in the United States has been characterized as overly reactive and dependent on emergency department care for safety net coverage, with opportunity for improvement around discharge planning and high readmissions and emergency department bounce-back rates. Community paramedicine is a recent healthcare innovation that enables proactive visitation of patients at home, often shortly after emergency department and hospital discharge. We establish the first optimization-based framework to study efficiencies in the management and operation of a community paramedicine program. The collective innovations of our modeling include i) a novel hierarchical objective function with the goals of fairly increasing patient welfare, lowering hospital costs, and reducing readmissions and emergency department visits, ii) a new constraint set that ensures priority same-day visits for emergent patients, and iii) a further extension of our model to determine the minimum supplemental resources necessary to ensure feasibility in a single optimization formulation. Our medical-need based objective function prioritizes patients based on their clinical features and seeks to select and schedule patient visits and route healthcare providers to maximize overall patient welfare while favoring shorter tours. We use our methods to develop managerial insights via computational experiments on a variety of test instances based on real data from a hospital system in Upstate New York. We are able to identify optimal and nearly optimal tours that efficiently select, route, and schedule patients in reasonable timeframes. Our results lead to insights that can support managerial decisions about establishing (and improving existing) community paramedicine programs.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"719-746"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10157611","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

Leveraging the E-commerce footprint for the surveillance of healthcare utilization. 利用电子商务足迹监控医疗保健使用情况。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-08-29 DOI: 10.1007/s10729-023-09645-4

Manuel Hermosilla, Jian Ni, Haizhong Wang, Jin Zhang

{"title":"Leveraging the E-commerce footprint for the surveillance of healthcare utilization.","authors":"Manuel Hermosilla, Jian Ni, Haizhong Wang, Jin Zhang","doi":"10.1007/s10729-023-09645-4","DOIUrl":"10.1007/s10729-023-09645-4","url":null,"abstract":"The utilization of healthcare services serves as a barometer for current and future health outcomes. Even in countries with modern healthcare IT infrastructure, however, fragmentation and interoperability issues hinder the (short-term) monitoring of utilization, forcing policymakers to rely on secondary data sources, such as surveys. This deficiency may be particularly problematic during public health crises, when ensuring proper and timely access to healthcare acquires special importance. We show that, in specific contexts, online pharmacies' digital footprint data may contain a strong signal of healthcare utilization. As such, online pharmacy data may enable utilization surveillance, i.e., the monitoring of short-term changes in utilization levels in the population. Our analysis takes advantage of the scenario created by the first wave of the Covid-19 pandemic in Mainland China, where the virus' spread lead to pervasive and deep reductions of healthcare service utilization. Relying on a large sample of online pharmacy transactions with full national coverage, we first detect variation that is strongly consistent with utilization reductions across geographies and over time. We then validate our claims by contrasting online pharmacy variation against credit-card transactions for medical services. Using machine learning methods, we show that incorporating online pharmacy data into the models significantly improves the accuracy of utilization surveillance estimates.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"604-625"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10167152","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 assignment-dial-a-ride-problem. 分配问题。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-10-21 DOI: 10.1007/s10729-023-09655-2

Chane-Haï Timothée, Vercraene Samuel, Monteiro Thibaud

{"title":"The assignment-dial-a-ride-problem.","authors":"Chane-Haï Timothée, Vercraene Samuel, Monteiro Thibaud","doi":"10.1007/s10729-023-09655-2","DOIUrl":"10.1007/s10729-023-09655-2","url":null,"abstract":"In this paper, we present the first Assignment-Dial-A-Ride problem motivated by a real-life problem faced by medico-social institutions in France. Every day, disabled people use ride-sharing services to go to an appropriate institution where they receive personal care. These institutions have to manage their staff to meet the demands of the people they receive. They have to solve three interconnected problems: the routing for the ride-sharing services; the assignment of disabled people to institutions; and the staff size in the institutions. We formulate a general Assignment-Dial-A-Ride problem to solve all three at the same time. We first present a matheuristic that iteratively generates routes using a large neighborhood search in which these routes are selected with a mixed integer linear program. After being validated on two special cases in the literature, the matheuristic is applied to real instances in three different areas in France. Several managerial results are derived. In particular, it is found that the amount of cost reduction induced by the people assignment is equivalent to the amount of cost reduction induced by the sharing of vehicles between institutions.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"770-784"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49676901","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

Classification of patients with chronic disease by activation level using machine learning methods. 使用机器学习方法按激活水平对慢性病患者进行分类。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-10-12 DOI: 10.1007/s10729-023-09653-4

Onur Demiray, Evrim D Gunes, Ercan Kulak, Emrah Dogan, Seyma Gorcin Karaketir, Serap Cifcili, Mehmet Akman, Sibel Sakarya

{"title":"Classification of patients with chronic disease by activation level using machine learning methods.","authors":"Onur Demiray, Evrim D Gunes, Ercan Kulak, Emrah Dogan, Seyma Gorcin Karaketir, Serap Cifcili, Mehmet Akman, Sibel Sakarya","doi":"10.1007/s10729-023-09653-4","DOIUrl":"10.1007/s10729-023-09653-4","url":null,"abstract":"Patient Activation Measure (PAM) measures the activation level of patients with chronic conditions and correlates well with patient adherence behavior, health outcomes, and healthcare costs. PAM is increasingly used in practice to identify patients needing more support from the care team. We define PAM levels 1 and 2 as low PAM and investigate the performance of eight machine learning methods (Logistic Regression, Lasso Regression, Ridge Regression, Random Forest, Gradient Boosted Trees, Support Vector Machines, Decision Trees, Neural Networks) to classify patients. Primary data collected from adult patients (n=431) with Diabetes Mellitus (DM) or Hypertension (HT) attending Family Health Centers in Istanbul, Turkey, is used to test the methods. [Formula: see text] of patients in the dataset have a low PAM level. Classification performance with several feature sets was analyzed to understand the relative importance of different types of information and provide insights. The most important features are found as whether the patient performs self-monitoring, smoking and exercise habits, education, and socio-economic status. The best performance was achieved with the Logistic Regression algorithm, with Area Under the Curve (AUC)=0.72 with the best performing feature set. Alternative feature sets with similar prediction performance are also presented. The prediction performance was inferior with an automated feature selection method, supporting the importance of using domain knowledge in machine learning.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"626-650"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41199292","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

Determining optimal COVID-19 testing center locations and capacities. 确定新冠肺炎检测中心的最佳位置和容量。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-11-07 DOI: 10.1007/s10729-023-09656-1

Esma Akgun, Sibel A Alumur, F Safa Erenay

{"title":"Determining optimal COVID-19 testing center locations and capacities.","authors":"Esma Akgun, Sibel A Alumur, F Safa Erenay","doi":"10.1007/s10729-023-09656-1","DOIUrl":"10.1007/s10729-023-09656-1","url":null,"abstract":"We study the problem of determining the locations and capacities of COVID-19 specimen collection centers to efficiently improve accessibility to polymerase chain reaction testing during surges in testing demand. We develop a two-echelon multi-period location and capacity allocation model that determines optimal number and locations of pop-up testing centers, capacities of the existing centers as well as assignments of demand regions to these centers, and centers to labs. The objective is to minimize the total number of delayed appointments and specimens subject to budget, capacity, and turnaround time constraints, which will in turn improve the accessibility to testing. We apply our model to a case study for locating COVID-19 testing centers in the Region of Waterloo, Canada using data from the Ontario Ministry of Health, public health databases, and medical literature. We also test the performance of the model under uncertain demand and analyze its outputs under various scenarios. Our analyses provide practical insights to the public health decision-makers on the timing of capacity expansions and the locations for the new pop-up centers. According to our results, the optimal strategy is to dynamically expand the existing specimen collection center capacities and prevent bottlenecks by locating pop-up facilities. The optimal locations of pop-ups are among the densely populated areas that are in proximity to the lab and a subset of those locations are selected with the changes in demand. A comparison with a static approach promises up to 39% cost savings under high demand using the developed multi-period model.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"748-769"},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71480901","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

Correction to: Select, route and schedule: optimizing community paramedicine service delivery with mandatory visits and patient prioritization. 更正：选择、路线和时间表：通过强制就诊和患者优先顺序优化社区护理服务提供。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 DOI: 10.1007/s10729-023-09651-6

Shima Azizi, Özge Aygül, Brenton Faber, Sharon Johnson, Renata Konrad, Andrew C Trapp

引用次数: 0

Managing a multi-panel clinic with heterogeneous patients. 管理一个由不同患者组成的多小组诊所。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-12-01 Epub Date: 2023-11-06 DOI: 10.1007/s10729-023-09658-z

Hao-Wei Chen

引用次数: 0