Operations Research for Health Care最新文献

{"title":"Improving patient satisfaction and outpatient diagnostic center efficiency using novel online real-time scheduling","authors":"Varun Jain ,&nbsp;Usha Mohan ,&nbsp;Zach Zacharia ,&nbsp;Nada R. Sanders","doi":"10.1016/j.orhc.2022.100338","DOIUrl":"10.1016/j.orhc.2022.100338","url":null,"abstract":"<div><p>We develop a novel online real-time scheduling algorithm with applications for healthcare diagnostic centers to deal with walk-in patients based on a set of constraints on the sequence of tests and resources. The problem is especially significant at healthcare centers in developing and emerging nations, such as India, where appointment schedules do not work. Within this realistic context, our objective is to improve patient satisfaction by reducing waiting time and improve diagnostic center performance through better utilization of the constrained resources. We propose a Mixed Integer Linear Programming (MILP) formulation to represent diagnostic centers as a Flow and Open Shop, to capture the system dynamics of the Flexible Hybrid Shop Scheduling Problem. We then develop a novel Online Genetic Algorithm (OGA) capable of solving real life large scale problems, as Open Shop scheduling problems are NP-hard. The developed OGA is first validated for small instances against a theoretical lower bound and the MILP model using CPLEX solver for flow time and makespan. The OGA is then empirically validated with data collected from two diagnostic centers of different sizes and configurations. For both centers, the developed OGA shows significant improvement compared to the simulation model. This research offers an important contribution to both literature and practice as it is one of the first to model the patient scheduling problem as an online real-time process. Implementing the developed OGA would help diagnostic centers significantly improve time estimates, thus reducing actual patient time and improving the efficiency of the system. Most importantly, the OGA is generalizable beyond healthcare to a broad range of environments that share Hybrid Shop characteristics.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"32 ","pages":"Article 100338"},"PeriodicalIF":2.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44767831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimization approach to panel size management","authors":"William Harrington ,&nbsp;Paul A. Rubin ,&nbsp;Lihui Bai","doi":"10.1016/j.orhc.2021.100313","DOIUrl":"10.1016/j.orhc.2021.100313","url":null,"abstract":"<div><p><span>We consider the panel size management problem that aims to balance workloads among providers within a health care<span> system, where the ratio of a primary care<span> provider’s load to their own daily full capacity is used as a measure for overload or underload. While transferring patients from their existing providers to others is the means to achieve workload balance, several practical restrictions prohibit transfer if patients have </span></span></span>multiple chronic conditions or should there be other reasons to discourage assignment changes. We also consider patients requests for specific characteristics of providers (e.g., same gender, same geographic location). In case the current system is greatly stressed even with patient panel reassignment, we allow for hiring new providers strategically at appropriate practice groups so that load balancing is achieved for the system (with new hires) and the utilization of no provider, existing or newly hired, exceeds a threshold value. Data analysis on provider panels from a Louisville regional health care system is performed and is used in developing an integer linear program model for the problem. Three case studies based on the data for the regional health care system show that the proposed model is effective in achieving load balancing and preventing physician burnout.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"31 ","pages":"Article 100313"},"PeriodicalIF":2.1,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46918674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling the association between weather and short-term demand for children’s intensive care transport services during winter in the South East of England","authors":"Samuel Livingstone ,&nbsp;Christina Pagel ,&nbsp;Zejing Shao ,&nbsp;Elise Randle ,&nbsp;Padmanabhan Ramnarayan","doi":"10.1016/j.orhc.2021.100327","DOIUrl":"10.1016/j.orhc.2021.100327","url":null,"abstract":"<div><p>Data from a paediatric intensive care transport service based in the South East of England between 2006 and 2018 are studied using generalised additive models to investigate the effects of extreme weather on demand in winter. Noticeable increases in daily demand for the service are uncovered after periods of extreme weather, and can be partitioned into two characteristically different phenomena, most pronounced at 2 days and 7 days after a period of particularly low temperature combined with either high or low humidity. The effect is more visible when virus prevalence is accounted for, showing that demand can increase by as much as 30% 7 days after a period of low temperature and low humidity, and 20% 2 days after a period of low temperature and high humidity.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"31 ","pages":"Article 100327"},"PeriodicalIF":2.1,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43740894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simulation–optimization approach for measuring emergency department resilience in times of crisis","authors":"Sorour Farahi,&nbsp;Khodakaram Salimifard","doi":"10.1016/j.orhc.2021.100326","DOIUrl":"10.1016/j.orhc.2021.100326","url":null,"abstract":"<div><p>Crisis occurrence in the healthcare context is, for different reasons, a phenomenon that happens abundantly. The priority of the healthcare system during a crisis is to provide quality care and superior services to the injured people. However, given the usually extreme severity of the crisis that results in a significant number of injured people, proper and timely responsiveness of healthcare systems is a challenging issue This study proposes a novel framework using a hybrid simulation–optimization approach to measure the healthcare responsiveness in crisis to address this real-world problem. This paper closely connects operations research techniques to critical systems thinking notions to evaluate the behavior of a system in the face of crisis. Since all arriving casualties to the hospital are first taken to the emergency department (ED), the ED in a case study is used to illustrate the performance of the presented approach. We designed seven crisis scenarios and one scenario of the ED system in a normal situation and modeled them using discrete-event simulation (DES). Patients’ interarrival times act as the driver of workload experienced in ED during crisis scenarios of varying severity. For crisis simulation scenarios that are unable to cope with the severity of the crisis, we developed an optimization model in an optimization tool to determine the optimal configuration of resources. The optimal configuration can improve healthcare resilience. The results show that an interarrival time of 13.8 min is the maximum threshold, below which feasible solutions could not be found, and the ED system is likely to collapse.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"31 ","pages":"Article 100326"},"PeriodicalIF":2.1,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46840246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating cascading events for emergency departments in Baltimore City using a two-state Markov model","authors":"Xu Zhang ,&nbsp;Bruce Golden ,&nbsp;Edward Wasil ,&nbsp;Laura Pimentel ,&nbsp;Jon Mark Hirshon","doi":"10.1016/j.orhc.2021.100324","DOIUrl":"10.1016/j.orhc.2021.100324","url":null,"abstract":"<div><p>The event of high emergency department (ED) utilization or inaccessibility to the ED may result in hospital after hospital in a city not accepting new patients in need of urgent medical care. We call this a cascading event. In this paper, we investigate cascading events among 11 EDs in Baltimore City in 2018 and 2019 using a two-state Markov model. Additionally, the transition probabilities are used to monitor the evolution of cascading events. Meanwhile, we predict the expected remaining hours in each state. After we calculate and compare the probabilities of having a cascading event for each ED, we finally identify the similarity and heterogeneity among EDs using cluster analysis. The findings of our study reveal that the continuous yellow alerts at Johns Hopkins Hospital, Johns Hopkins Bayview Medical Center (JH Bayview), Sinai Hospital, and the University of Maryland Medical Center (UMMC) are associated with a large chance of having a cascading event in the city that affects all 11 hospitals. Weekdays dramatically increased chances of having a cascading event.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"31 ","pages":"Article 100324"},"PeriodicalIF":2.1,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49424038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operating room planning and scheduling for outpatients and inpatients: A review and future research","authors":"Lien Wang ,&nbsp;Erik Demeulemeester ,&nbsp;Nancy Vansteenkiste ,&nbsp;Frank E. Rademakers","doi":"10.1016/j.orhc.2021.100323","DOIUrl":"10.1016/j.orhc.2021.100323","url":null,"abstract":"<div><p>In hospitals, surgeries are treated either on an outpatient or on an inpatient basis. Outpatients are normally routine patients that enter and leave the hospital on the same day, while inpatients who need more complex surgeries have to stay overnight. More recently, a shift from inpatient surgery to outpatient surgery is occurring due to scientific progress in anaesthesia and surgical techniques. Identifying possible similarities and differences between outpatient surgery scheduling and inpatient surgery scheduling can serve as a valuable decision-making foundation for practitioners and for operations researchers to efficiently schedule patients for surgery in the surgical department. This paper provides the first literature review on comparing outpatient surgery scheduling with inpatient surgery scheduling. The literature published between 2000 and 2020 that explicitly mentions either scheduling setting is included and it is analyzed from three dimensions, i.e., the uncertainty incorporation, the research methodology, and a scheduling performance comparison between both settings. We find that outpatient surgery can observe better results in many of the performance measures (i.e., operating room utilization, overtime, and patient cancellation rate) as opposed to inpatient surgery. This is due to the fact that inpatient surgery duration is longer and more variable and to the presence of more emergency patients, although there is a higher likelihood of no-shows for outpatients. Moreover, we identify future research directions that provide opportunities for expanding existing methodologies and especially for narrowing the gap between theory and practice.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"31 ","pages":"Article 100323"},"PeriodicalIF":2.1,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43950083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of OR/MS models on care planning for frail and elderly patients","authors":"Elizabeth Williams,&nbsp;Daniel Gartner,&nbsp;Paul Harper","doi":"10.1016/j.orhc.2021.100325","DOIUrl":"10.1016/j.orhc.2021.100325","url":null,"abstract":"<div><h3>Context</h3><p>With an ageing population, there is an increased demand on public health services and on long-term-care facilities. It is not uncommon for frail and elderly patients to spend longer in hospital or require more support within the community, often due to multi-morbidities. Many health services are faced with the complex problem as to how to administer the best care for the frail and elderly whilst best managing limited health resources.</p></div><div><h3>Objective</h3><p><span>This paper focuses on the literature concerning frail and elderly patient pathways within both hospital and community care settings with the use of Operations Research and Management Science (OR/MS) methods. To cover a wide range of specialities, the following additional subject areas have been included: Geriatrics<span> and Gerontology, Health Policy and Services, Industrial Engineering, and </span></span>Medical Informatics, to synthesise the work on modelling the application of care for frail and elderly patients. This review paper also analyses trends in the research literature and identifies gaps for future study.</p></div><div><h3>Methods</h3><p>A set of criteria has been established in which a systematic search was performed against to identify literature from 2000 to 2020. In total 62 publications were identified as applicable and were categorised methodologically and analysed. Common features of the papers including hospital setting, research aims and planning decisions have been identified and discussed.</p></div><div><h3>Results</h3><p>The results from the analysis reveal that this field of study is increasing with over 47% of papers having been published since 2015. The main findings suggest three areas of future research. Firstly, focus should be on modelling pathways holistically, with collaboration from both hospitals and community care settings. Secondly, work should be conducted on patient outcomes of these modelled pathways to highlight the increase in quality of care. Thirdly, there is potential for a wider variety of OR/MS methods to be utilised across the whole pathway. These three areas will reduce pressure on health services which are currently facing rising demands with limited resources.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"31 ","pages":"Article 100325"},"PeriodicalIF":2.1,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48951612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controllable and non-controllable factors to measure performance in primary care practices under Medicare alternative payment models","authors":"Jennifer Mendoza-Alonzo ,&nbsp;José Zayas-Castro ,&nbsp;Armin Lüer-Villagra","doi":"10.1016/j.orhc.2021.100312","DOIUrl":"10.1016/j.orhc.2021.100312","url":null,"abstract":"<div><p><span>We analyze the two recent Medicare alternative payment models, the comprehensive primary care plus (CPC+) and the primary care first (PCF). Both models comprise fee-for-service, traditional capitation, and pay-for-performance (P4P) components. The main objective of these reimbursement models is to advance toward value-based care. However, the models confer some hesitations since the P4P component is based on factors not entirely controlled by the practice, increasing the potential admission of healthier patients and affecting the profit of small primary care practices. We have modified the P4P component in both models to include a non-controllable agent (the hierarchical condition category score) and a controllable factor (the Bice–Boxerman continuity of care index) through a probabilistic classification model to predict hospital admissions. This study aims to determine the impact of adjusting the P4P component, in the CPC+ and PCF reimbursement models, on the profit per team, revenue for performance per team, and severity of admitted patients. We develop a mixed-integer programming formulation and analyze, using a 2k factorial design, the reimbursement models and the main elements of their adjusted P4P components (i.e., the probabilistic classification model coefficients and hospital admission threshold). The results indicate that the coefficients of the probabilistic classification model and the hospital admission threshold have a significant effect on the profit and revenue for performance per team. There is also a tendency of the PCF to admit less severe patients than the CPC+. Yet, the effects are more notable in the PCF payment model because the proportion of P4P in the total revenue under the CPC+ is minimal (16.5% versus </span><span><math><mrow><mo>&lt;</mo><mn>1</mn><mtext>%</mtext></mrow></math></span>). Similarly, the PCF’s downside is its sensitivity to P4P changes, displaying high variability in the output variables under analysis.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"30 ","pages":"Article 100312"},"PeriodicalIF":2.1,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.orhc.2021.100312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45634462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling the effect of first-wave COVID-19 on mental health services","authors":"B.J. Murch ,&nbsp;J.A. Cooper ,&nbsp;T.J. Hodgett ,&nbsp;E.L. Gara ,&nbsp;J.S. Walker ,&nbsp;R.M. Wood","doi":"10.1016/j.orhc.2021.100311","DOIUrl":"10.1016/j.orhc.2021.100311","url":null,"abstract":"<div><p>During the first wave of the COVID-19 pandemic it emerged that the nature and magnitude of demand for mental health services was changing. Considerable increases were expected to follow initial lulls as treatment was sought for new and existing conditions following relaxation of ‘lockdown’ measures. For this to be managed by the various services that constitute a mental health system, it would be necessary to complement such projections with assessments of capacity, in order to understand the propagation of demand and the value of any consequent mitigations. This paper provides an account of exploratory modelling undertaken within a major UK healthcare system during the first wave of the pandemic, when actionable insights were in short supply and decisions were made under much uncertainty. In understanding the impact on post-lockdown operational performance, the objective was to evaluate the efficacy of two considered interventions against a baseline ‘do nothing’ scenario. In doing so, a versatile and purpose-built discrete time simulation model was developed, calibrated and used by a multi-disciplinary project working group. The solution, representing a multi-node, multi-server queueing network with reneging, is implemented in open-source software and is freely and publicly available.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"30 ","pages":"Article 100311"},"PeriodicalIF":2.1,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.orhc.2021.100311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10413507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue on Healthcare Analytics","authors":"Christina Büsing,&nbsp;Sebastian Rachuba,&nbsp;Clemens Thielen","doi":"10.1016/j.orhc.2021.100309","DOIUrl":"10.1016/j.orhc.2021.100309","url":null,"abstract":"","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"30 ","pages":"Article 100309"},"PeriodicalIF":2.1,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.orhc.2021.100309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47614475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}