Health Care Management Science最新文献_第7页

Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic. 英国医院在第一波和第二波冠状病毒大流行期间的表现分析。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-05-09 DOI: 10.1007/s10729-023-09634-7

Timo Kuosmanen, Yong Tan, Sheng Dai

{"title":"Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic.","authors":"Timo Kuosmanen, Yong Tan, Sheng Dai","doi":"10.1007/s10729-023-09634-7","DOIUrl":"10.1007/s10729-023-09634-7","url":null,"abstract":"The coronavirus infection COVID-19 killed millions of people around the world in 2019-2022. Hospitals were in the forefront in the battle against the pandemic. This paper proposes a novel approach to assess the effectiveness of hospitals in saving lives. We empirically estimate the production function of COVID-19 deaths among hospital inpatients, applying Heckman's two-stage approach to correct for the bias caused by a large number of zero-valued observations. We subsequently assess performance of hospitals based on regression residuals, incorporating contextual variables to convex quantile regression. Data of 187 hospitals in England over a 35-week period from April to December 2020 is divided in two sub-periods to compare the structural differences between the first and second waves of the pandemic. The results indicate significant performance improvement during the first wave, however, learning by doing was offset by the new mutated virus straits during the second wave. While the elderly patients were at significantly higher risk during the first wave, their expected mortality rate did not significantly differ from that of the general population during the second wave. Our most important empirical finding concerns large and systematic performance differences between individual hospitals: larger units proved more effective in saving lives, and hospitals in London had a lower mortality rate than the national average.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"447-460"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10195617","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 surgical waiting lists through dynamic priority scoring. 通过动态优先级评分管理手术等待名单。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-06-28 DOI: 10.1007/s10729-023-09648-1

Jack Powers, James M McGree, David Grieve, Ratna Aseervatham, Suzanne Ryan, Paul Corry

{"title":"Managing surgical waiting lists through dynamic priority scoring.","authors":"Jack Powers, James M McGree, David Grieve, Ratna Aseervatham, Suzanne Ryan, Paul Corry","doi":"10.1007/s10729-023-09648-1","DOIUrl":"10.1007/s10729-023-09648-1","url":null,"abstract":"Prioritising elective surgery patients under the Australian three-category system is inherently subjective due to variability in clinician decision making and the potential for extraneous factors to influence category assignment. As a result, waiting time inequities can exist which may lead to adverse health outcomes and increased morbidity, especially for patients deemed to be low priority. This study investigated the use of a dynamic priority scoring (DPS) system to rank elective surgery patients more equitably, based on a combination of waiting time and clinical factors. Such a system enables patients to progress on the waiting list in a more objective and transparent manner, at a rate relative to their clinical need. Simulation results comparing the two systems indicate that the DPS system has potential to assist in managing waiting lists by standardising waiting times relative to urgency category, in addition to improving waiting time consistency for patients of similar clinical need. In clinical practice, this system is likely to reduce subjectivity, increase transparency, and improve overall efficiency of waiting list management by providing an objective metric to prioritise patients. Such a system is also likely to increase public trust and confidence in the systems used to manage waiting lists.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"533-557"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10249174","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

Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways. 急诊科新冠肺炎分诊2.0：分析和人工智能如何转变用于预测临床路径的人工算法。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-07-10 DOI: 10.1007/s10729-023-09647-2

Christina C Bartenschlager, Milena Grieger, Johanna Erber, Tobias Neidel, Stefan Borgmann, Jörg J Vehreschild, Markus Steinbrecher, Siegbert Rieg, Melanie Stecher, Christine Dhillon, Maria M Ruethrich, Carolin E M Jakob, Martin Hower, Axel R Heller, Maria Vehreschild, Christoph Wyen, Helmut Messmann, Christiane Piepel, Jens O Brunner, Frank Hanses, Christoph Römmele

{"title":"Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways.","authors":"Christina C Bartenschlager, Milena Grieger, Johanna Erber, Tobias Neidel, Stefan Borgmann, Jörg J Vehreschild, Markus Steinbrecher, Siegbert Rieg, Melanie Stecher, Christine Dhillon, Maria M Ruethrich, Carolin E M Jakob, Martin Hower, Axel R Heller, Maria Vehreschild, Christoph Wyen, Helmut Messmann, Christiane Piepel, Jens O Brunner, Frank Hanses, Christoph Römmele","doi":"10.1007/s10729-023-09647-2","DOIUrl":"10.1007/s10729-023-09647-2","url":null,"abstract":"The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"412-429"},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10485125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10249635","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

Health information exchange network under collaboration, cooperation, and competition: A game-theoretic approach. 协作、合作和竞争下的卫生信息交换网络：一种博弈论方法。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-06-21 DOI: 10.1007/s10729-023-09640-9

Rawan Shabbar, Hiroki Sayama

{"title":"Health information exchange network under collaboration, cooperation, and competition: A game-theoretic approach.","authors":"Rawan Shabbar, Hiroki Sayama","doi":"10.1007/s10729-023-09640-9","DOIUrl":"10.1007/s10729-023-09640-9","url":null,"abstract":"Health Information Exchange (HIE) network allows securely accessing and sharing healthcare-related information among healthcare providers (HCPs) and payers. HIE services are provided by a non-profit/profit organizations under several subscription plans options. A few studies have addressed the sustainability of the HIE network such that HIE providers, HCPs, and payers remain profitable in the long term. However, none of these studies addressed the coexistence of multiple HIE providers in the network. Such coexistence may have a huge impact on the behavior of healthcare systems in terms of adoption rate and HIE pricing strategies. In addition, in spite of all the effort to maintain cooperation between HIE providers, there is still a chance of competition among them in the market. Possible competition among service providers leads to many concerns about the HIE network sustainability and behavior. In this study, a game-theoretic approach to model the HIE market is proposed. Game-theory is used to simulate the behavior of the three different HIE network agents in the HIE market: HIE providers, HCPs, and payers. Pricing strategies and adoption decisions are optimized using a Linear Programming (LP) mathematical model. Results show that the relation between HIEs in the market is crucial to HCP/Payer adoption decision specially to small HCPs. A small change in the discount rate proposed by a competitive HIE provider will highly affect the decision of HCP/payers to join the HIE network. Finally, competition opened the opportunity for more HCPs to join the network due to reduced pricing. Furthermore, collaborative HIEs provided better performance compared to cooperative in terms of profit and HCP adoption rate by sharing their overall costs and revenues.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"516-532"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10602700","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

Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions. 预测病房级床位需求以帮助疫情资源规划：经验教训和未来方向。

IF 2.3 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-05-18 DOI: 10.1007/s10729-023-09639-2

Michael R Johnson, Hiten Naik, Wei Siang Chan, Jesse Greiner, Matt Michaleski, Dong Liu, Bruno Silvestre, Ian P McCarthy

{"title":"Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions.","authors":"Michael R Johnson, Hiten Naik, Wei Siang Chan, Jesse Greiner, Matt Michaleski, Dong Liu, Bruno Silvestre, Ian P McCarthy","doi":"10.1007/s10729-023-09639-2","DOIUrl":"10.1007/s10729-023-09639-2","url":null,"abstract":"During the COVID-19 pandemic, there has been considerable research on how regional and country-level forecasting can be used to anticipate required hospital resources. We add to and build on this work by focusing on ward-level forecasting and planning tools for hospital staff during the pandemic. We present an assessment, validation, and deployment of a working prototype forecasting tool used within a modified Traffic Control Bundling (TCB) protocol for resource planning during the pandemic. We compare statistical and machine learning forecasting methods and their accuracy at one of the largest hospitals (Vancouver General Hospital) in Canada against a medium-sized hospital (St. Paul's Hospital) in Vancouver, Canada through the first three waves of the COVID-19 pandemic in the province of British Columbia. Our results confirm that traditional statistical and machine learning (ML) forecasting methods can provide valuable ward-level forecasting to aid in decision-making for pandemic resource planning. Using point forecasts with upper 95% prediction intervals, such forecasting methods would have provided better accuracy in anticipating required beds on COVID-19 hospital units than ward-level capacity decisions made by hospital staff. We have integrated our methodology into a publicly available online tool that operationalizes ward-level forecasting to aid with capacity planning decisions. Importantly, hospital staff can use this tool to translate forecasts into better patient care, less burnout, and improved planning for all hospital resources during pandemics.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"477-500"},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10192806","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

Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients. 评估和实施实时床位分配策略，以减少外科住院患者的等待时间。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-06-09 DOI: 10.1007/s10729-023-09638-3

Aleida Braaksma, Martin S Copenhaver, Ana C Zenteno, Elizabeth Ugarph, Retsef Levi, Bethany J Daily, Benjamin Orcutt, Kathryn M Turcotte, Peter F Dunn

{"title":"Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients.","authors":"Aleida Braaksma, Martin S Copenhaver, Ana C Zenteno, Elizabeth Ugarph, Retsef Levi, Bethany J Daily, Benjamin Orcutt, Kathryn M Turcotte, Peter F Dunn","doi":"10.1007/s10729-023-09638-3","DOIUrl":"10.1007/s10729-023-09638-3","url":null,"abstract":"Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients' arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches-beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"501-515"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10174583","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

Intraday dynamic rescheduling under patient no-shows. 在患者未出现的情况下进行日间动态重新安排。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-07-10 DOI: 10.1007/s10729-023-09643-6

Aditya Shetty, Harry Groenevelt, Vera Tilson

{"title":"Intraday dynamic rescheduling under patient no-shows.","authors":"Aditya Shetty, Harry Groenevelt, Vera Tilson","doi":"10.1007/s10729-023-09643-6","DOIUrl":"10.1007/s10729-023-09643-6","url":null,"abstract":"Patient no-shows are a major source of uncertainty for outpatient clinics. A common approach to hedge against the effect of no-shows is to overbook. The trade-off between patient's waiting costs and provider idling/overtime costs determines the optimal level of overbooking. Existing work on appointment scheduling assumes that appointment times cannot be updated once they have been assigned. However, advances in communication technology and the adoption of online (as opposed to in-person) appointments make it possible for appointments to be flexible. In this paper, we describe an intraday dynamic rescheduling model that adjusts upcoming appointments based on observed no-shows. We formulate the problem as a Markov Decision Process in order to compute the optimal pre-day schedule and the optimal policy to update the schedule for every scenario of no-shows. We also propose an alternative formulation based on the idea of 'atomic' actions that allows us to apply a shortest path algorithm to solve for the optimal policy more efficiently. Based on a numerical study using parameter estimates from existing literature, we find that intraday dynamic rescheduling can reduce expected cost by 15% compared to static scheduling.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"583-598"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10530715","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

Assessing the impact of COVID-19 on the performance of organ transplant services using data envelopment analysis. 利用数据包络分析评估COVID-19对器官移植服务绩效的影响。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-06-01 DOI: 10.1007/s10729-023-09637-4

Márcia N F Manoel, Sérgio P Santos, Carla A F Amado

{"title":"Assessing the impact of COVID-19 on the performance of organ transplant services using data envelopment analysis.","authors":"Márcia N F Manoel, Sérgio P Santos, Carla A F Amado","doi":"10.1007/s10729-023-09637-4","DOIUrl":"https://doi.org/10.1007/s10729-023-09637-4","url":null,"abstract":"Organ transplant is one of the best options for many medical conditions, and in many cases, it may be the only treatment option. Recent evidence suggests, however, that the COVID-19 pandemic might have detrimentally affected the provision of this type of healthcare services. The main purpose of this article is to use Data Envelopment Analysis and the Malmquist Index to assess the impact that the pandemic caused by the novel coronavirus SARS-CoV-2 had on the provision of solid organ transplant services. To this purpose, we use three complementary models, each focusing on specific aspects of the organ donation and transplantation process, and data from Brazil, which has one of the most extensive public organ transplant programs in the world. Using data from 17 States plus the Federal District, the results of our analysis show a significant drop in the performance of the services in terms of the organ donation and transplantation process from 2018 to 2020, but the results also indicate that not all aspects of the process and States were equally affected. Furthermore, by using different models, this research also allows us to gain a more comprehensive and informative assessment of the performance of the States in delivering this type of service and identify opportunities for reciprocal learning, expanding our knowledge on this important issue and offering opportunities for further research.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"217-237"},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9606293","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 two-stage partial fixing approach for solving the residency block scheduling problem. 一种求解住院医师块调度问题的两阶段部分固定方法。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-06-01 DOI: 10.1007/s10729-023-09631-w

Junhong Guo, William Pozehl, Amy Cohn

{"title":"A two-stage partial fixing approach for solving the residency block scheduling problem.","authors":"Junhong Guo, William Pozehl, Amy Cohn","doi":"10.1007/s10729-023-09631-w","DOIUrl":"https://doi.org/10.1007/s10729-023-09631-w","url":null,"abstract":"We consider constructing feasible annual block schedules for residents in a medical training program. We must satisfy coverage requirements to guarantee an acceptable staffing level for different services in the hospital as well as education requirements to ensure residents receive appropriate training to pursue their individual (sub-)specialty interests. The complex requirement structure makes this resident block scheduling problem a complicated combinatorial optimization problem. Solving a conventional integer program formulation for certain practical instances directly using traditional solution techniques will result in unacceptably slow performance. To address this, we propose a partial fixing approach, which completes the schedule construction iteratively through two sequential stages. The first stage focuses on the resident assignments for a small set of predetermined services through solving a much smaller and easier problem relaxation, while the second stage completes the rest of the schedule construction after fixing those assignments specified by the first stage's solution. We develop cut generation mechanisms to prune off the bad decisions made by the first stage if infeasibility arises in the second stage. We additionally propose a network-based model to assist us with an effective service selection for the first stage to work on the corresponding resident assignments to achieve an efficient and robust performance of the proposed two-stage iterative approach. Experiments using real-world inputs from our clinical collaborator show that our approach can speed up the schedule construction at least 5 times for all instances and even over 100 times for some huge-size instances compared to applying traditional techniques directly.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"363-393"},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9605267","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 two-stage stochastic optimization framework to allocate operating room capacity in publicly-funded hospitals under uncertainty. 不确定条件下公办医院手术室容量配置的两阶段随机优化框架

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-06-01 DOI: 10.1007/s10729-023-09644-5

Morteza Lalmazloumian, M Fazle Baki, Majid Ahmadi

{"title":"A two-stage stochastic optimization framework to allocate operating room capacity in publicly-funded hospitals under uncertainty.","authors":"Morteza Lalmazloumian, M Fazle Baki, Majid Ahmadi","doi":"10.1007/s10729-023-09644-5","DOIUrl":"https://doi.org/10.1007/s10729-023-09644-5","url":null,"abstract":"Surgery demand is an uncertain parameter in addressing the problem of surgery block allocations, and its typical variability should be considered to ensure the feasibility of surgical planning. We develop two models, a stochastic recourse programming model and a two-stage stochastic optimization (SO) model with incorporated risk measure terms in the objective functions to determine a planning decision that is made to allocate surgical specialties to operating rooms (ORs). Our aim is to minimize the costs associated with postponements and unscheduled demands as well as the inefficient use of OR capacity. The results of these models are compared using a case of a real-life hospital to determine which model better copes with uncertainty. We propose a novel framework to transform the SO model based on its deterministic counterpart. Three SO models are proposed with respect to the variability and infeasibility of the measures of the objective function to encode the construction of the SO framework. The analysis of the experimental results demonstrates that the SO model offers better performance under a highly volatile demand environment than the recourse model. The originality of this work lies in its use of SO transformation framework and its development of stochastic models to address the problem of surgery capacity allocation based on a real case.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"238-260"},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9605633","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