Health Care Management Science最新文献

{"title":"Real-time management of intra-hospital patient transport requests.","authors":"Vinicius M Ton, Nathália C O da Silva, Angel Ruiz, José E Pécora, Cassius T Scarpin, Valérie Bélenger","doi":"10.1007/s10729-024-09667-6","DOIUrl":"10.1007/s10729-024-09667-6","url":null,"abstract":"<p><p>This paper addresses the management of patients' transportation requests within a hospital, a very challenging problem where requests must be scheduled among the available porters so that patients arrive at their destination timely and the resources invested in patient transport are kept as low as possible. Transportation requests arrive during the day in an unpredictable manner, so they need to be scheduled in real-time. To ensure that the requests are scheduled in the best possible manner, one should also reconsider the decisions made on pending requests that have not yet been completed, a process that will be referred to as rescheduling. This paper proposes several policies to trigger and execute the rescheduling of pending requests and three approaches (a mathematical formulation, a constructive heuristic, and a local search heuristic) to solve each rescheduling problem. A simulation tool is proposed to assess the performance of the rescheduling strategies and the proposed scheduling methods to tackle instances inspired by a real mid-size hospital. Compared to a heuristic that mimics the way requests are currently handled in our partner hospital, the best combination of scheduling method and rescheduling strategy produces an average 5.7 minutes reduction in response time and a 13% reduction in the percentage of late requests. Furthermore, since the total distance walked by porters is substantially reduced, our experiments demonstrate that it is possible to reduce the number of porters - and therefore the operating costs - without reducing the current level of service.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140039154","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}

{"title":"Visibility-based layout of a hospital unit - An optimization approach.","authors":"Uttam Karki, Pratik J Parikh","doi":"10.1007/s10729-024-09670-x","DOIUrl":"10.1007/s10729-024-09670-x","url":null,"abstract":"<p><p>A patient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. The medical literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. To fill this gap, we adopt an interdisciplinary approach that combines the human field of view with facility layout design approaches. Specifically, we propose a bi-objective optimization model that jointly determines the optimal (i) location of a nurse in a nursing station and (ii) orientation of a patient's bed in a room for a given layout. The two objectives are maximizing the total visibility of all patients across patient rooms and minimizing inequity in visibility among those patients. We consider three different layout types, L-shaped, I-shaped, and Radial; these shapes exhibit the section of an inpatient unit that a nurse oversees. To estimate visibility, we employ the ray casting algorithm to quantify the visible target in a room when viewed by the nurse from the nursing station. The algorithm considers nurses' horizontal visual field and their depth of vision. Owing to the difficulty in solving the bi-objective model, we also propose a Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to find (near) optimal solutions. Our findings suggest that the Radial layout appears to outperform the other two layouts in terms of the visibility-based objectives. We found that with a Radial layout, there can be an improvement of up to 50% in equity measure compared to an I-shaped layout. Similar improvements were observed when compared to the L-shaped layout as well. Further, the position of the patient's bed plays a role in maximizing the visibility of the patient's room. Insights from our work will enable understanding and quantifying the relationship between a physical layout and the corresponding provider-to-patient visibility to reduce adverse events.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140854711","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}

{"title":"Integrated procurement and reprocessing planning for reusable medical devices with a limited shelf life.","authors":"Steffen Rickers, Florian Sahling","doi":"10.1007/s10729-024-09664-9","DOIUrl":"10.1007/s10729-024-09664-9","url":null,"abstract":"<p><p>We present a new model formulation for a multiproduct dynamic order quantity problem with product returns and a reprocessing option. The optimization considers the limited shelf life of sterile medical devices as well as the capacity constraints of reprocessing and sterilization resources. The time-varying demand is known in advance and must be satisfied by purchasing new medical devices or by reprocessing used and expired devices. The objective is to determine a feasible procurement and reprocessing plan that minimizes the incurred costs. The problem is solved in a heuristic manner in two steps. First, we use a Dantzig-Wolfe reformulation of the underlying problem, and a column generation approach is applied to tighten the lower bound. In the next step, the obtained lower bound is transformed into a feasible solution using CPLEX. Our numerical results illustrate the high solution quality of this approach. The comparison with a simulation based on the first-come-first-served principle shows the advantage of integrated planning.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11258087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139546058","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}

{"title":"Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984-2022.","authors":"Dinesh R Pai, Fatma Pakdil, Nasibeh Azadeh-Fard","doi":"10.1007/s10729-024-09669-4","DOIUrl":"10.1007/s10729-024-09669-4","url":null,"abstract":"<p><p>This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals. We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140027932","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}

{"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":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"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}

{"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":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"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}

{"title":"An optimization framework for large-scale screening under limited testing capacity with application to COVID-19.","authors":"Jiayi Lin, Hrayer Aprahamian, G. Golovko","doi":"10.1007/s10729-024-09671-w","DOIUrl":"https://doi.org/10.1007/s10729-024-09671-w","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140659342","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}

{"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":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"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}

{"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":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"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}

{"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":"<p><p>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.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":null,"pages":null},"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}