Health Care Management Science最新文献

Forecasting to support EMS tactical planning: what is important and what is not. 支持紧急医疗服务战术规划的预测：什么是重要的，什么是不重要的。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-10-19 DOI: 10.1007/s10729-024-09690-7

Mostafa Rezaei, Armann Ingolfsson

{"title":"Forecasting to support EMS tactical planning: what is important and what is not.","authors":"Mostafa Rezaei, Armann Ingolfsson","doi":"10.1007/s10729-024-09690-7","DOIUrl":"https://doi.org/10.1007/s10729-024-09690-7","url":null,"abstract":"Forecasting emergency medical service (EMS) call volumes is critical for resource allocation and planning. The development of many commercial and free software packages has made a variety of forecasting methods accessible. Practitioners, however, are left with little guidance on selecting the most appropriate method for their needs. Using 5 years of data from 3 cities in Alberta, we compute exponential smoothing and benchmark forecasts for 8-hour periods for each ambulance station catchment area and with a forecast horizon of two weeks-a spatio-temporal resolution appropriate for tactical planning. The methods that we consider differ on three spectra: the number and type of time-series components, whether forecasts are computed individually or jointly, and the way in which forecasts at a specific resolution are converted to forecasts at the resolution of interest. We find that it is important to include a weekly seasonal component when forecasting EMS demand. Multiplicative seasonality, however, shows no benefit over additive seasonality. Adding other time-series components (e.g., trend, ARMA errors, Box-Cox transformation) does not improve performance. Spatial resolutions of station catchment area and lower, and temporal resolution of 4-24 hours perform similarly. We adapt an existing hierarchical forecasting framework to a two-dimensional spatio-temporal hierarchy, but find that hierarchical reconciliation of forecasts does not improve performance at the forecast resolution of interest for tactical planning. Neither does jointly forecasting time series. We show that added complexity does not materially improve forecasting performance. The simple methods that we find perform well are easy to implement and interpret, making implementation in practice more likely. In a simulation study we alter the empirical weekly patterns and demonstrate how extreme differences between the weekly seasonality patterns of different regions cause hierarchically-reconciled bottom-up approaches to outperform top-down approaches.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142463887","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

Health care management science for underserved populations. 针对服务不足人群的医疗保健管理科学。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-10-16 DOI: 10.1007/s10729-024-09687-2

Itamar Megiddo, Sarang Deo, Alec Morton, Sheetal Silal

引用次数: 0

Optimization of testing protocols to screen for COVID-19: a multi-objective model. 优化筛查 COVID-19 的测试方案：多目标模型。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-10-11 DOI: 10.1007/s10729-024-09688-1

Hadi Moheb-Alizadeh, Donald P Warsing, Richard E Kouri, Sajjad Taghiyeh, Robert B Handfield

{"title":"Optimization of testing protocols to screen for COVID-19: a multi-objective model.","authors":"Hadi Moheb-Alizadeh, Donald P Warsing, Richard E Kouri, Sajjad Taghiyeh, Robert B Handfield","doi":"10.1007/s10729-024-09688-1","DOIUrl":"https://doi.org/10.1007/s10729-024-09688-1","url":null,"abstract":"In this paper we develop a new multi-objective simulated annealing (MOSA) algorithm to generate optimal testing protocols for infectious diseases, using the COVID-19 pandemic as our context. A SEIR (susceptible-exposed-infected-recovered) epidemiological model is embedded as the computational platform for our MOSA algorithm to optimize testing protocols for screening across three joint objectives: minimum cost of test materials, minimum total infections over the testing horizon, and minimum number of false negatives over the horizon. We demonstrate the application of this optimization tool to recommend screening protocols for K-12 school districts in the U.S. State of North Carolina. Our approach is scalable by population coverage and can be employed at the level of individual school districts or regional collections of districts, individual schools or collections of schools across a district, business sites, or nursing homes, among other congregate settings where individuals may be screened prior to gaining entry to the site. The algorithm can be solved two ways, generating either independent optimal protocols across individual testing locations, or a common protocol covering all locations in the collection of testing sites. Our findings can be used to inform policy decisions to guide the development of effective testing strategies for controlling the spread of COVID-19 or other pandemic diseases in a wide range of congregate settings across various geographic regions.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142400160","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 novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study. 医疗和物流不确定性下肾脏分配问题的新型两阶段网络数据包络分析模型：一项实际案例研究。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-10-01 DOI: 10.1007/s10729-024-09683-6

Farhad Hamidzadeh, Mir Saman Pishvaee, Naeme Zarrinpoor

{"title":"A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study.","authors":"Farhad Hamidzadeh, Mir Saman Pishvaee, Naeme Zarrinpoor","doi":"10.1007/s10729-024-09683-6","DOIUrl":"https://doi.org/10.1007/s10729-024-09683-6","url":null,"abstract":"Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345536","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 benefits (or detriments) of adapting to demand disruptions in a hospital pharmacy with supply chain disruptions. 医院药房在供应链中断的情况下适应需求中断的好处（或坏处）。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-24 DOI: 10.1007/s10729-024-09686-3

Lauren L Czerniak, Mariel S Lavieri, Mark S Daskin, Eunshin Byon, Karl Renius, Burgunda V Sweet, Jennifer Leja, Matthew A Tupps

{"title":"The benefits (or detriments) of adapting to demand disruptions in a hospital pharmacy with supply chain disruptions.","authors":"Lauren L Czerniak, Mariel S Lavieri, Mark S Daskin, Eunshin Byon, Karl Renius, Burgunda V Sweet, Jennifer Leja, Matthew A Tupps","doi":"10.1007/s10729-024-09686-3","DOIUrl":"https://doi.org/10.1007/s10729-024-09686-3","url":null,"abstract":"Supply chain disruptions and demand disruptions make it challenging for hospital pharmacy managers to determine how much inventory to have on-hand. Having insufficient inventory leads to drug shortages, while having excess inventory leads to drug waste. To mitigate drug shortages and waste, hospital pharmacy managers can implement inventory policies that account for supply chain disruptions and adapt these inventory policies over time to respond to demand disruptions. Demand disruptions were prevalent during the Covid-19 pandemic. However, it remains unclear how a drug's shortage-waste weighting (i.e., concern for shortages versus concern for waste) as well as the duration of and time between supply chain disruptions influence the benefits (or detriments) of adapting to demand disruptions. We develop an adaptive inventory system (i.e., inventory policies change over time) and conduct an extensive numerical analysis using real-world demand data from the University of Michigan's Central Pharmacy to address this research question. For a fixed mean duration of and mean time between supply chain disruptions, we find a drug's shortage-waste weighting dictates the magnitude of the benefits (or detriments) of adaptive inventory policies. We create a ranking procedure that provides a way of discerning which drugs are of most concern and illustrates which policies to update given that a limited number of inventory policies can be updated. When applying our framework to over 300 drugs, we find a decision-maker needs to update a very small proportion of drugs (e.g., <math><mrow><mo><</mo> <mn>5</mn> <mo>%</mo></mrow> </math> ) at any point in time to get the greatest benefits of adaptive inventory policies.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142307645","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

Strategic placement of volunteer responder system defibrillators. 战略性地安置志愿响应系统除颤器。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-10 DOI: 10.1007/s10729-024-09685-4

Robin Buter, Arthur Nazarian, Hendrik Koffijberg, Erwin W Hans, Remy Stieglis, Rudolph W Koster, Derya Demirtas

{"title":"Strategic placement of volunteer responder system defibrillators.","authors":"Robin Buter, Arthur Nazarian, Hendrik Koffijberg, Erwin W Hans, Remy Stieglis, Rudolph W Koster, Derya Demirtas","doi":"10.1007/s10729-024-09685-4","DOIUrl":"https://doi.org/10.1007/s10729-024-09685-4","url":null,"abstract":"Volunteer responder systems (VRS) alert and guide nearby lay rescuers towards the location of an emergency. An application of such a system is to out-of-hospital cardiac arrests, where early cardiopulmonary resuscitation (CPR) and defibrillation with an automated external defibrillator (AED) are crucial for improving survival rates. However, many AEDs remain underutilized due to poor location choices, while other areas lack adequate AED coverage. In this paper, we present a comprehensive data-driven algorithmic approach to optimize deployment of (additional) public-access AEDs to be used in a VRS. Alongside a binary integer programming (BIP) formulation, we consider two heuristic methods, namely Greedy and Greedy Randomized Adaptive Search Procedure (GRASP), to solve the gradual Maximal Covering Location (MCLP) problem with partial coverage for AED deployment. We develop realistic gradually decreasing coverage functions for volunteers going on foot, by bike, or by car. A spatial probability distribution of cardiac arrest is estimated using kernel density estimation to be used as input for the models and to evaluate the solutions. We apply our approach to 29 real-world instances (municipalities) in the Netherlands. We show that GRASP can obtain near-optimal solutions for large problem instances in significantly less time than the exact method. The results indicate that relocating existing AEDs improves the weighted average coverage from 36% to 49% across all municipalities, with relative improvements ranging from 1% to 175%. For most municipalities, strategically placing 5 to 10 additional AEDs can already provide substantial improvements.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142285860","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

Editorial: management science for pandemic prevention, preparedness, and response. 社论：大流行病预防、准备和应对的管理科学。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-06-19 DOI: 10.1007/s10729-024-09674-7

Hrayer Aprahamian, Vedat Verter, Manaf Zargoush

引用次数: 0

Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set. 利用德国手术室基准计划的潜力进行规划：随时可用的手术流程数据集。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-05-02 DOI: 10.1007/s10729-024-09672-9

Grigory Korzhenevich, Anne Zander

{"title":"Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set.","authors":"Grigory Korzhenevich, Anne Zander","doi":"10.1007/s10729-024-09672-9","DOIUrl":"10.1007/s10729-024-09672-9","url":null,"abstract":"We present a freely available data set of surgical case mixes and surgery process duration distributions based on processed data from the German Operating Room Benchmarking initiative. This initiative collects surgical process data from over 320 German, Austrian, and Swiss hospitals. The data exhibits high levels of quantity, quality, standardization, and multi-dimensionality, making it especially valuable for operating room planning in Operations Research. We consider detailed steps of the perioperative process and group the data with respect to the hospital's level of care, the surgery specialty, and the type of surgery patient. We compare case mixes for different subgroups and conclude that they differ significantly, demonstrating that it is necessary to test operating room planning methods in different settings, e.g., using data sets like ours. Further, we discuss limitations and future research directions. Finally, we encourage the extension and foundation of new operating room benchmarking initiatives and their usage for operating room planning.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140848511","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

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":null,"pages":null},"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