Operations Research for Health Care最新文献

{"title":"Solving a real-world nurse rostering problem by Simulated Annealing","authors":"Sara Ceschia ,&nbsp;Luca Di Gaspero ,&nbsp;Vincenzo Mazzaracchio ,&nbsp;Giuseppe Policante ,&nbsp;Andrea Schaerf","doi":"10.1016/j.orhc.2023.100379","DOIUrl":"10.1016/j.orhc.2023.100379","url":null,"abstract":"<div><p>Designing high quality nurse rostering plans is essential for health care facilities in order to guarantee efficiency, safety and quality-of-care balanced with staff well-being.</p><p>We introduce a new real-world formulation for the nurse rostering problem, arising in many Italian healthcare institutions, which has been developed in collaboration with a primary software company in the field. It considers nurses with different skills, special shifts depending on the skills, time work-load limits, and different types of days-off. In addition, preferences and incompatibilities between nurses are taken into account.</p><p>We propose a MIP model and a local search method, driven by a Simulated Annealing metaheuristic, based on a combination of two neighborhoods. The solution method was tested on 34 real-world instances coming from various healthcare institutions in North Italy. The dataset is available at <span>https://bitbucket.org/satt/nrp-instances</span><svg><path></path></svg>, along with our best solutions.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"36 ","pages":"Article 100379"},"PeriodicalIF":2.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42925353","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":"Operations research contribution to totally automated radiotherapy treatment planning: A noncoplanar beam angle and fluence map optimization engine based on optimization models and algorithms","authors":"J.M. Dias ,&nbsp;H. Rocha ,&nbsp;P. Carrasqueira ,&nbsp;B.C. Ferreira ,&nbsp;T. Ventura ,&nbsp;M.C. Lopes","doi":"10.1016/j.orhc.2023.100378","DOIUrl":"10.1016/j.orhc.2023.100378","url":null,"abstract":"<div><p>The planning of radiotherapy treatments is based on the use of mathematical optimization models and algorithms. A treatment plan will correspond to an admissible solution to a problem that is defined by the medical prescription. The medical prescription defines the constraints that have to be satisfied, and that are patient dependent. To find a treatment plan complying with all the defined constraints, an objective function is built, having a set of parameters that can be tuned. In the clinical practice, the objective function parameters are tuned through a trial-and-error procedure. One common way of defining this objective function is to consider as parameters weights that are related with the importance of the corresponding structures of interest (volumes to treat and organs to spare), as well as upper and lower bounds that are related with the constraints defined by the medical prescription. Some treatment options, like the set of irradiation directions (beam angles), are usually defined beforehand by the planner, considering previous experiences with similar cases. This trial-and-error process is lengthy, since it can take up to several hours to calculate an admissible treatment plan for a single patient. There have been some research efforts to automate the treatment planning procedure, releasing the planner for other important tasks in the radiotherapy treatment workflow and guaranteeing the consistent calculation of high-quality plans. In this work derivative-free optimization algorithms are integrated with fuzzy inference systems that automatically tune the objective function parameters so that an admissible solution is found. This fully automated approach was tested and assessed considering six head-and-neck cancer cases already treated at the Portuguese Institute of Oncology at Coimbra (IPOC). For the clinical cases tested, comparisons between different treatment plans clearly favor the proposed approach. For a similar tumor coverage, it was possible to improve the sparing of the spinal cord, brainstem and parotids. Automating radiotherapy treatment planning can contribute to improved treatment plans in a consistent way.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"36 ","pages":"Article 100378"},"PeriodicalIF":2.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45096161","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 long-term forecasting and simulation model for strategic planning of hospital bed capacity","authors":"Timo Latruwe ,&nbsp;Marlies Van der Wee ,&nbsp;Pieter Vanleenhove ,&nbsp;Joke Devriese ,&nbsp;Sofie Verbrugge ,&nbsp;Didier Colle","doi":"10.1016/j.orhc.2022.100375","DOIUrl":"10.1016/j.orhc.2022.100375","url":null,"abstract":"<div><p>Growing healthcare needs leverage the potential savings of using resources efficiently. To that end, ProMoBed is a comprehensive model that supports strategic planning of bed capacity in inpatient hospitals. The model consists of an extrapolation and simulation component, the former supplying input for the latter. The extrapolation model forecasts admission rates and the average Length of Stay for pathology groups, and corrects for demographic changes. Subsequently, the simulation model emulates the demand for bed capacity, and makes service-level based bed capacity suggestions. Additionally, the model uses the Shapley value principle to disaggregate the effects on demand for inpatient days due to different causes. Results from the extrapolation model are applied to regions in Belgium, showing expected divergence in inpatient day demand evolution.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"36 ","pages":"Article 100375"},"PeriodicalIF":2.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43325498","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":"Health Outcome Predictive Modelling in Intensive Care Units","authors":"Chengqian Xian, C. P. Souza, Felipe F. Rodrigues","doi":"10.1101/2022.12.15.22283527","DOIUrl":"https://doi.org/10.1101/2022.12.15.22283527","url":null,"abstract":"The literature in Intensive Care Units (ICUs) data analysis focuses on predictions of length-of-stay (LOS) and mortality based on patient acuity scores such as Acute Physiology and Chronic Health Evaluation (APACHE), Sequential Organ Failure Assessment (SOFA), to name a few. Unlike ICUs in other areas around the world, ICUs in Ontario, Canada, collect two primary intensive care scoring scales, a therapeutic acuity score called the \"Multiple Organs Dysfunctional Score\" (MODS) and a nursing workload score called the \"Nine Equivalents Nursing Manpower Use Score\" (NEMS). The dataset analyzed in this study contains patients' NEMS and MODS scores measured upon patient admission into the ICU and other characteristics commonly found in the literature. Data were collected between January 1st, 2015 and May 31st, 2021, at two teaching hospital ICUs in Ontario, Canada. In this work, we developed logistic regression, random forests (RF) and neural network (NN) models for mortality (discharged or deceased) and LOS (short or long stay) predictions. Considering the effect of mortality outcome on LOS, we also combined mortality and LOS to create a new categorical health outcome called LMClass (short stay & discharged, short stay & deceased, or long stay without specifying mortality outcomes), and then applied multinomial regression and RF for its prediction. Five repetitions corresponding to five random starting points have been done in RF and NN for model optimization, and 5-fold cross-validation (CV) was also carried out for model stability investigation. Results show that logistic regression is the optimal model in mortality prediction with the highest area under the curve (AUC) of 0.795 and also in LMClass prediction with the highest accuracy of 0.630. In contrast, in LOS prediction, RF outperforms the other methods with the highest AUC of 0.689. This study also demonstrates that MODS and NEMS, as well as their components measured upon patient arrival, significantly contribute to health outcome prediction in ICUs.","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49433765","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":"Integrated master surgery and outpatient clinic scheduling","authors":"Thomas Reiten Bovim ,&nbsp;Anita Abdullahu ,&nbsp;Henrik Andersson ,&nbsp;Anders N. Gullhav","doi":"10.1016/j.orhc.2022.100358","DOIUrl":"10.1016/j.orhc.2022.100358","url":null,"abstract":"<div><p>In this paper, we study an integrated master surgery and outpatient clinic scheduling problem, motivated by the situation at the Orthopaedic Department at St. Olav’s Hospital, Trondheim. During a treatment process, the patients require one or several consultations at the outpatient clinic, and potentially a surgery in one of the operating rooms. The physicians perform both consultations and surgeries, and coordinating the two facilities is challenging. The surgeons are trained to handle different surgical specialties, and they differ in experience. The overall goal is to schedule the specialties, and a number of qualified surgeons, to time slots in the outpatient clinic and operating rooms through the week, to efficiently handle the patient demand. Our main contribution is an optimisation model for solving the integrated master surgery and outpatient clinic scheduling problem. In addition to allocating specialties and a number of surgeons, the model also schedules activity types (surgery categories and outpatient clinic consultation types) to the time slots. These can guide the operational scheduling of individual patients at a later stage. A computational study is performed, demonstrating the use of the optimisation model to provide a set of master schedules, based on a set of different resource capacity cases. We develop a simulation model for evaluating the master schedules in an operational setting, and three different operational scheduling policies are compared. We conclude that scheduling patients to activities governed primarily by the optimisation model solution outperforms a FIFO scheduling policy based only on specialty.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"35 ","pages":"Article 100358"},"PeriodicalIF":2.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211692322000194/pdfft?md5=154d160125aceecfcdd6d9556d7a8f8c&pid=1-s2.0-S2211692322000194-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45028394","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":"A simulation optimization approach for planning and scheduling in operating rooms for elective and urgent surgeries","authors":"Yao Xiao,&nbsp;Reena Yoogalingam","doi":"10.1016/j.orhc.2022.100366","DOIUrl":"10.1016/j.orhc.2022.100366","url":null,"abstract":"<div><p>Improving operating room efficiency has become one of the most important goals for health care providers given growing expenditures in managing hospital operations. Effective appointment schedules, which minimize the cost of patient waiting time and operating room idle time and overtime, play an important role in terms of improving efficiency. The variability inherent in surgical procedure times and random arrival of urgent cases significantly complicates the scheduling process. In this study, we compare several strategies for scheduling outpatient surgical procedures in the presence of urgent arrivals. First, a simulation optimization approach is used to find heuristic solutions for a surgery scheduling problem with a heterogeneous set of elective and urgent procedures in a multiple operating room setting. Second, a discrete-event simulation model is used to numerically evaluate how different sequencing and allocation policies perform for multiple surgery procedure types. Historical surgery procedure data for a hospital providing day surgeries over a two-year period from the Canadian Institute for Health Information (CIHI) database will be used to provide empirical support for the input parameters of the model.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"35 ","pages":"Article 100366"},"PeriodicalIF":2.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42747511","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 predictive model for the post-pandemic delay in elective treatment","authors":"Romy Nehme,&nbsp;Alena Puchkova,&nbsp;Ajith Parlikad","doi":"10.1016/j.orhc.2022.100357","DOIUrl":"10.1016/j.orhc.2022.100357","url":null,"abstract":"<div><p>The COVID-19 pandemic had a major impact on healthcare systems across the world. In the United Kingdom, one of the strategies used by hospitals to cope with the surge in patients infected with SARS-Cov-2 was to cancel a vast number of elective treatments planned and limit its resources for non-critical patients. This resulted in a 30% drop in the number of people joining the waiting list in 2020–2021 versus 2019–2020. Once the pandemic subsides and resources are freed for elective treatment, the expectation is that the patients failing to receive treatment throughout the pandemic would trigger a significant backlog on the waiting list post-pandemic with major repercussions to patient health and quality of life. As the nation emerges from the worst phase of the pandemic, hospitals are focusing on strategies to prioritise patients for elective treatments. A key challenge in this context is the ability to quantify the expected backlog and predict the delays experienced by patients as an outcome of the prioritisation policies. This study presents an approach based on discrete-event simulation to predict the elective waiting list backlog along with the delay in treatment based on a predetermined prioritisation policy. The model is demonstrated using data on the endoscopy waiting list at Cambridge University Hospitals. The model shows that 21% of the patients on the waiting list will experience a delay less than 18-weeks, the acceptable threshold set by the National Health Service (NHS). A longer-term scenario analysis based on the model reveals investment in NHS resources will have a significant positive outcome for addressing the waiting lists. The model presented in this paper has the potential to be an invaluable tool for post-pandemic planning for hospitals around the world that are facing a crisis of treatment backlog.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"34 ","pages":"Article 100357"},"PeriodicalIF":2.1,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9446608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10411071","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":"Intensive care unit/step-down unit queuing game with length of stay decisions","authors":"Yawo M. Kobara ,&nbsp;Felipe F. Rodrigues ,&nbsp;Camila P.E. de Souza ,&nbsp;David Andrews Stanford","doi":"10.1016/j.orhc.2022.100349","DOIUrl":"https://doi.org/10.1016/j.orhc.2022.100349","url":null,"abstract":"<div><p>In this paper, length of stay (LOS) competition between two servers in tandem without buffer between them is investigated using queuing games. This system typifies the relationship between the intensive care unit<span> (ICU) and the step-down unit (SDU) of a hospital. We model and analyze the equilibrium LOS decision under four different games (one cooperative and three non-cooperative games) as follows: (i) both servers cooperate; (ii) the servers do not cooperate and make decisions simultaneously; (iii) the servers do not cooperate and the first server, the ICU, is the leader (ICU Stackelberg); (iv) the servers do not cooperate and the second server, the SDU, is the leader (SDU Stackelberg). The payoff of the ICU is expressed as the difference between the service benefit and the waiting in queue penalty, while that of the SDU is the difference between the service benefit and the overstay penalty. The results show that LOS decisions of each server depends critically on the payoff function’s form and the exogenous demand. Secondly, with a linear payoff function, the SDU is only beneficial to the system if the unit cost is greater than the unit reward at the ICU. Our results revealed also that payoffs depend on the substitutability in both ICU Stackelberg and SDU Stackelberg games. When most of the LOS is spent at the ICU unit. Our results suggest that the critical care pathway performs better under coordination and or leadership at the ICU level.</span></p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"34 ","pages":"Article 100349"},"PeriodicalIF":2.1,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91956666","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":"Intensive care unit/step-down unit queuing game with length of stay decision","authors":"Y. Kobara, Felipe F. Rodrigues, C. de Souza, D. Stanford","doi":"10.1016/j.orhc.2022.100349","DOIUrl":"https://doi.org/10.1016/j.orhc.2022.100349","url":null,"abstract":"","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45179436","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":"Discrete event simulation for emergency department modelling: A systematic review of validation methods","authors":"Evgueniia Doudareva,&nbsp;Michael Carter","doi":"10.1016/j.orhc.2022.100340","DOIUrl":"https://doi.org/10.1016/j.orhc.2022.100340","url":null,"abstract":"<div><h3>Background:</h3><p>Discrete event simulation has been widely used for decades to model the Emergency Department (ED) patient flow, understand system bottlenecks, and analyse resource capacity planning constraints. Given the complexity and constraints related to modelling the ED setting, such as data availability, there is often a gap in prescribing the correct validation approach.</p></div><div><h3>Objectives:</h3><p>The purpose of this review study is to come up with practical guidelines for employing specific validation techniques by comparing the available “best practice” simulation validation approaches against the approaches commonly found in published ED Discrete Event Simulation (DES) studies.</p></div><div><h3>Methods:</h3><p>We conducted a systematic review of the peer-reviewed literature to identify DES studies of patient flow within hospital EDs across the globe. Our search strategy focused on two main domains of knowledge associated with the current study: DES and ED. In total, we selected 90 studies a basis for the analysis. Additionally, we have identified a total of 7 papers focused on best practice approaches in validation.</p></div><div><h3>Results:</h3><p>A plurality of studies only discuss a single type of validation (data-led) at 30%, closely followed by none (23%), and data-led paired with qualitative validation, but no verification (at 22%). LOS is the most common validation metric, with 47% of studies that used data-led validation selecting length of stay (LOS) as the key validation metric. The next most frequently validated metric is throughput (9%), followed by time to triage (TTT) (8%). “Confidence Interval” and “% Difference” are by far the most common, with 32% of studies employing the former, and 22% employing the latter. Remaining techniques tend to be used more sporadically, with hypothesis testing, correlation coefficient, Student t-test, and Welch’s two-sample t-test being the most frequent (5% to 9% of studies).</p></div><div><h3>Conclusion:</h3><p>Based on the reviewed studies, we propose guidelines for the validation procedure given five “levels” of available data quality. The guideline incorporates both the analysis of best practice literature, as well as the trends for validation based on the review of 90 generic and specific simulation studies.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"33 ","pages":"Article 100340"},"PeriodicalIF":2.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211692322000029/pdfft?md5=3d7a2c1c4a4e6fc437823bcd86dbc8ea&pid=1-s2.0-S2211692322000029-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91712313","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}