Health Care Management Science最新文献_第2页

Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning. 用可解释的机器学习预测门诊病人以不满意度为考虑因素的候诊时间。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-06-01 DOI: 10.1007/s10729-024-09676-5

Jongkyung Shin, Donggi Augustine Lee, Juram Kim, Chiehyeon Lim, Byung-Kwan Choi

{"title":"Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning.","authors":"Jongkyung Shin, Donggi Augustine Lee, Juram Kim, Chiehyeon Lim, Byung-Kwan Choi","doi":"10.1007/s10729-024-09676-5","DOIUrl":"10.1007/s10729-024-09676-5","url":null,"abstract":"Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time. Here, underestimating waiting times can cause patient dissatisfaction, so preventing this in predictive models is necessary. To address this issue, we propose a framework considering dissatisfaction for estimating the waiting time in an outpatient department. In our framework, we leverage asymmetric loss functions to ensure robustness against underestimation. We also propose a dissatisfaction-aware asymmetric error score (DAES) to determine an appropriate model by considering the trade-off between underestimation and accuracy. Finally, Shapley additive explanation (SHAP) is applied to interpret the relationship trained by the model, enabling decision makers to use this information for improving outpatient service operations. We apply our framework in the endocrinology metabolism department and neurosurgery department in one of the largest hospitals in South Korea. The use of asymmetric functions prevents underestimation in the model, and with the proposed DAES, we can strike a balance in selecting the best model. By using SHAP, we can analytically interpret the waiting time in outpatient service (e.g., the length of the queue affects the waiting time the most) and provide explanations about the expected waiting time to patients. The proposed framework aids in improving operations, considering practical application in hospitals for real-time patient notification and minimizing patient dissatisfaction. Given the significance of managing hospital operations from the perspective of patients, this work is expected to contribute to operations improvement in health service practices.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"370-390"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141186080","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

Examining chronic kidney disease screening frequency among diabetics: a POMDP approach. 研究糖尿病患者的慢性肾病筛查频率：一种 POMDP 方法。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-06-05 DOI: 10.1007/s10729-024-09677-4

Chou-Chun Wu, Yiwen Cao, Sze-Chuan Suen, Eugene Lin

{"title":"Examining chronic kidney disease screening frequency among diabetics: a POMDP approach.","authors":"Chou-Chun Wu, Yiwen Cao, Sze-Chuan Suen, Eugene Lin","doi":"10.1007/s10729-024-09677-4","DOIUrl":"10.1007/s10729-024-09677-4","url":null,"abstract":"Forty percent of diabetics will develop chronic kidney disease (CKD) in their lifetimes. However, as many as 50% of these CKD cases may go undiagnosed. We developed screening recommendations stratified by age and previous test history for individuals with diagnosed diabetes and unknown proteinuria status by race and gender groups. To do this, we used a Partially Observed Markov Decision Process (POMDP) to identify whether a patient should be screened at every three-month interval from ages 30-85. Model inputs were drawn from nationally-representative datasets, the medical literature, and a microsimulation that integrates this information into group-specific disease progression rates. We implement the POMDP solution policy in the microsimulation to understand how this policy may impact health outcomes and generate an easily-implementable, non-belief-based approximate policy for easier clinical interpretability. We found that the status quo policy, which is to screen annually for all ages and races, is suboptimal for maximizing expected discounted future net monetary benefits (NMB). The POMDP policy suggests more frequent screening after age 40 in all race and gender groups, with screenings 2-4 times a year for ages 61-70. Black individuals are recommended for screening more frequently than their White counterparts. This policy would increase NMB from the status quo policy between $1,000 to $8,000 per diabetic patient at a willingness-to-pay of $150,000 per quality-adjusted life year (QALY).","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"391-414"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246850","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

A novel approach to forecast surgery durations using machine learning techniques. 利用机器学习技术预测手术持续时间的新方法。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-07-10 DOI: 10.1007/s10729-024-09681-8

Marco Caserta, Antonio García Romero

引用次数: 0

Health care management science - best paper of 2023. 医疗保健管理科学--2023 年最佳论文。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-01 DOI: 10.1007/s10729-024-09689-0

Greg Zaric

引用次数: 0

Enhancing affordability and profit in a non-cooperative, coordinated, hypothetical pediatric vaccine market via sequential optimization. 在一个非合作、协调、假设的儿科疫苗市场中，通过顺序优化提高可负担性和利润。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-06-29 DOI: 10.1007/s10729-024-09680-9

Bruno Alves-Maciel, Ruben A Proano

{"title":"Enhancing affordability and profit in a non-cooperative, coordinated, hypothetical pediatric vaccine market via sequential optimization.","authors":"Bruno Alves-Maciel, Ruben A Proano","doi":"10.1007/s10729-024-09680-9","DOIUrl":"10.1007/s10729-024-09680-9","url":null,"abstract":"This study considers a hypothetical global pediatric vaccine market where multiple coordinating entities make optimal procurement decisions on behalf of countries with different purchasing power. Each entity aims to improve affordability for its countries while maintaining a profitable market for vaccine producers. This study analyzes the effect of several factors on affordability and profitability, including the number of non-cooperative coordinating entities making procuring decisions, the number of market segments in which countries are grouped for tiered pricing purposes, how producers recover fixed production costs, and the procuring order of the coordinating entities. The study relies on a framework where entities negotiate sequentially with vaccine producers using a three-stage optimization process that solves a MIP and two LP problems to determine the optimal procurement plans and prices per dose that maximize savings for the entities' countries and profit for the vaccine producers. The study's results challenge current vaccine market dynamics and contribute novel alternative strategies to orchestrate the interaction of buyers, producers, and coordinating entities for enhancing affordability in a non-cooperative market. Key results show that the order in which the coordinating entities negotiate with vaccine producers and how the latter recuperate their fixed cost investments can significantly affect profitability and affordability. Furthermore, low-income countries can meet their demands more affordably by procuring vaccines through tiered pricing via entities coordinating many market segments. In contrast, upper-middle and high-income countries increase their affordability by procuring through entities with fewer and more extensive market segments. A procurement order that prioritizes entities based on the descending income level of their countries offers higher opportunities to increase affordability and profit when producers offer volume discounts.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"436-457"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141476508","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

Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach. 病人管理中的多资源分配和护理顺序分配：一种随机编程方法。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-05-30 DOI: 10.1007/s10729-024-09675-6

Xinyu Yao, Karmel S Shehadeh, Rema Padman

{"title":"Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach.","authors":"Xinyu Yao, Karmel S Shehadeh, Rema Padman","doi":"10.1007/s10729-024-09675-6","DOIUrl":"10.1007/s10729-024-09675-6","url":null,"abstract":"To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of allocating and sequencing multiple medical resources so that patients scheduled for clinical care can experience efficient and coordinated care with minimum total waiting time. We leverage highly granular location data on people and medical resources collected via Real-Time Location System technologies to identify dominant patient care pathways. A novel two-stage Stochastic Mixed Integer Linear Programming model is proposed to determine the optimal patient sequence based on the available resources according to the care pathways that minimize patients' expected total waiting time. The model incorporates the uncertainty in care activity duration via sample average approximation.We employ a Monte Carlo Optimization procedure to determine the appropriate sample size to obtain solutions that provide a good trade-off between approximation accuracy and computational time. Compared to the conventional deterministic model, our proposed model would significantly reduce waiting time for patients in the clinic by 60%, on average, with acceptable computational resource requirements and time complexity. In summary, this paper proposes a computationally efficient formulation for the multi-resource allocation and care sequence assignment optimization problem under uncertainty. It uses continuous assignment decision variables without timestamp and position indices, enabling the data-driven solution of problems with real-time allocation adjustment in a dynamic outpatient environment with complex clinical coordination constraints.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"352-369"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141174788","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

Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel. 利用神经网络元模型，通过基于仿真的多目标优化，管理急诊科的低危重病人。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-06-10 DOI: 10.1007/s10729-024-09678-3

Marco Boresta, Tommaso Giovannelli, Massimo Roma

{"title":"Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.","authors":"Marco Boresta, Tommaso Giovannelli, Massimo Roma","doi":"10.1007/s10729-024-09678-3","DOIUrl":"10.1007/s10729-024-09678-3","url":null,"abstract":"This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acuity patients, with the aim of minimizing patient waiting times and ED operating costs. We formulate this problem as a general multiobjective simulation-based optimization problem where some of the objectives are expensive black-box functions that can only be evaluated through a time-consuming simulation. To efficiently solve this problem, we propose a metamodeling approach that uses an artificial neural network to replace a black-box objective function with a suitable model. This approach allows us to obtain a set of Pareto optimal points for the multiobjective problem we consider, from which decision-makers can select the most appropriate solutions for different situations. We present the results of computational experiments conducted on a real case study involving the ED of a large hospital in Italy. The results show the reliability and effectiveness of our proposed approach, compared to the standard approach based on derivative-free optimization.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"415-435"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141295939","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

A systematic literature review of predicting patient discharges using statistical methods and machine learning. 利用统计方法和机器学习预测病人出院情况的系统性文献综述。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-07-22 DOI: 10.1007/s10729-024-09682-7

Mahsa Pahlevani, Majid Taghavi, Peter Vanberkel

{"title":"A systematic literature review of predicting patient discharges using statistical methods and machine learning.","authors":"Mahsa Pahlevani, Majid Taghavi, Peter Vanberkel","doi":"10.1007/s10729-024-09682-7","DOIUrl":"10.1007/s10729-024-09682-7","url":null,"abstract":"Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":"458-478"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141734005","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

A study of "left against medical advice" emergency department patients: an optimized explainable artificial intelligence framework. 急诊科 "违抗医嘱离院 "患者研究：优化的可解释人工智能框架。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-08-13 DOI: 10.1007/s10729-024-09684-5

Abdulaziz Ahmed, Khalid Y Aram, Salih Tutun, Dursun Delen

{"title":"A study of \"left against medical advice\" emergency department patients: an optimized explainable artificial intelligence framework.","authors":"Abdulaziz Ahmed, Khalid Y Aram, Salih Tutun, Dursun Delen","doi":"10.1007/s10729-024-09684-5","DOIUrl":"https://doi.org/10.1007/s10729-024-09684-5","url":null,"abstract":"The issue of left against medical advice (LAMA) patients is common in today's emergency departments (EDs). This issue represents a medico-legal risk and may result in potential readmission, mortality, or revenue loss. Thus, understanding the factors that cause patients to \"leave against medical advice\" is vital to mitigate and potentially eliminate these adverse outcomes. This paper proposes a framework for studying the factors that affect LAMA in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization-one of the main challenges of machine learning model development. Adaptive tabu simulated annealing (ATSA) metaheuristic algorithm is utilized for optimizing the parameters of extreme gradient boosting (XGB). The optimized XGB models are used to predict the LAMA outcomes for patients under treatment in ED. The designed algorithms are trained and tested using four data groups which are created using feature selection. The model with the best predictive performance is then interpreted using the SHaply Additive exPlanations (SHAP) method. The results show that best model has an area under the curve (AUC) and sensitivity of 76% and 82%, respectively. The best model was explained using SHAP method.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975577","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

Meritorious service awards - 2023. 荣誉服务奖 - 2023 年。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2024-06-15 DOI: 10.1007/s10729-024-09679-2

Greg Zaric

引用次数: 0