Operations Research for Health Care最新文献_第2页

Health outcome predictive modelling in intensive care units 重症监护病房的健康结局预测模型

IF 2.1

Operations Research for Health Care Pub Date : 2023-10-04 DOI: 10.1016/j.orhc.2023.100409

Chengqian Xian , Camila P.E. de Souza , Felipe F. Rodrigues

{"title":"Health outcome predictive modelling in intensive care units","authors":"Chengqian Xian , Camila P.E. de Souza , Felipe F. Rodrigues","doi":"10.1016/j.orhc.2023.100409","DOIUrl":"https://doi.org/10.1016/j.orhc.2023.100409","url":null,"abstract":"<div><p>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 networks (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, RF and NN for its prediction. Among the models evaluated, logistic regression for mortality prediction results in the highest area under the curve (AUC) of 0.795 and also for LMClass prediction 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.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"39 ","pages":"Article 100409"},"PeriodicalIF":2.1,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49894026","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}

引用次数: 0

A data-driven decision support tool to improve hospital bed cleaning logistics using discrete event simulation considering operators’ behaviour 一个数据驱动的决策支持工具，以改善医院病床清洁物流使用离散事件模拟考虑运营商的行为

IF 2.1

Operations Research for Health Care Pub Date : 2023-09-22 DOI: 10.1016/j.orhc.2023.100408

Gaspard Hosteins , Allan Larsen , Dario Pacino , Christian Michel Sørup

{"title":"A data-driven decision support tool to improve hospital bed cleaning logistics using discrete event simulation considering operators’ behaviour","authors":"Gaspard Hosteins , Allan Larsen , Dario Pacino , Christian Michel Sørup","doi":"10.1016/j.orhc.2023.100408","DOIUrl":"https://doi.org/10.1016/j.orhc.2023.100408","url":null,"abstract":"<div><p>Beds are a critical resource for hospitals, requiring effective management to ensure the quality of care for patients. Beds operate in a closed-loop circuit and must be thoroughly cleaned between patients’ arrivals to prevent infections. Hospitals must implement efficient logistics systems to collect, transport, store, and clean unclean beds from discharged patients. These systems must be robust and efficient to meet the varying bed supply needs, given the available resources such as beds, staff and machines. This study aims to develop a decision support tool to optimise bed cleaning logistics and ensure the availability of sterile beds for incoming patients at all times. The study is based on the bed flow and cleaning organisation of a Danish public hospital. A discrete event simulation model (DES) of the back-end bed flow has been developed. The paper also presents a tension level indicator to reflect the behaviour of cleaning staff when facing variations in demand and bed stock. Using the organisational set-up (staff schedules, policies, and bed fleet size), the DES model: (1) evaluates the ability to provide sterile beds in a reasonable time, (2) measures the stress on cleaning staff, and (3) visualises resource usage. This study illustrates how to incorporate the staff’s perceived workload and resulting behaviour into a DES model to capture the behavioural aspect of staff’s decision-making.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"39 ","pages":"Article 100408"},"PeriodicalIF":2.1,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49872411","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}

引用次数: 0

Optimization model for combining drug vials in the preparation of doses for outpatient 门诊给药配瓶优化模型

IF 2.1

Operations Research for Health Care Pub Date : 2023-09-01 DOI: 10.1016/j.orhc.2023.100401

Maria Clara de Oliveira Gê , Breno Barros Telles do Carmo , Fábio Francisco da Costa Fontes , Dario José Aloise

{"title":"Optimization model for combining drug vials in the preparation of doses for outpatient","authors":"Maria Clara de Oliveira Gê , Breno Barros Telles do Carmo , Fábio Francisco da Costa Fontes , Dario José Aloise","doi":"10.1016/j.orhc.2023.100401","DOIUrl":"10.1016/j.orhc.2023.100401","url":null,"abstract":"<div><p>Planning the use of chemotherapy drugs<span><span> in outpatient treatment is a complex problem due to the variability of cancer, resulting in different chemotherapy protocols. This process involves factors such as the cyclical nature of treatment protocols and clinical resources. Within this context, an optimization model is needed to plan the use of chemotherapy drugs in the preparation of doses for patient treatment, considering the operational particularities of treatment centers. This study proposed a linear programming model based on the multiple knapsack problem, in order to optimize the combination of different vials of chemotherapy drugs, minimizing the total cost of treatment. The model, based on the daily schedule of patients, provides the best combination of drug vials and supports the preparation process of each dose prescribed by the doctor for each patient, respecting the treatment protocol and resource limitations. The model was implemented in the CPLEX 12.9.0 application, and the computational tests were performed with real data. The results demonstrated that the costs when applying the model were 5% lower when compared to the current manner in which the </span>oncology pharmacy combines the drugs vials.</span></p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"38 ","pages":"Article 100401"},"PeriodicalIF":2.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45812024","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}

引用次数: 0

Generating balanced workload allocations in hospitals 在医院中生成平衡的工作量分配

IF 2.1

Operations Research for Health Care Pub Date : 2023-09-01 DOI: 10.1016/j.orhc.2023.100390

Pieter Smet

{"title":"Generating balanced workload allocations in hospitals","authors":"Pieter Smet","doi":"10.1016/j.orhc.2023.100390","DOIUrl":"10.1016/j.orhc.2023.100390","url":null,"abstract":"<div><p>As pressure on healthcare systems continues to increase, it is becoming more and more important for hospitals to properly manage the high workload levels of their staff. Ensuring a balanced workload allocation between various groups of employees in a hospital has been shown to contribute considerably towards creating sustainable working conditions. However, allocating work to different organizational units in a fair manner is not straightforward when it involves complex decision-making processes. In this paper we set out to balance the workload of heterogeneous hospital wards by optimizing the patient admission scheduling problem. Given the multi-period nature of patient admission scheduling, we introduce a new equity objective that captures both spatial (between hospital wards) and temporal (between days in the planning period) workload balancing. The resulting bi-objective problem is solved using an exact criterion space search algorithm. Our computational study employs problem instances that have been generated based on real-world data. The results demonstrate how spatially and temporally balanced workload allocations can be generated by minimizing the proposed equity objective. Moreover, we analyze sets of nondominated solutions to gain various insights into the trade-off between schedule cost and workload balance.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"38 ","pages":"Article 100390"},"PeriodicalIF":2.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46502507","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}

引用次数: 0

Optimal discharge of patients from intensive care via a data-driven policy learning framework 通过数据驱动的政策学习框架实现重症监护患者的最佳出院

IF 2.1

Operations Research for Health Care Pub Date : 2023-09-01 DOI: 10.1016/j.orhc.2023.100400

Fernando Lejarza , Jacob Calvert , Misty M. Attwood , Daniel Evans , Qingqing Mao

{"title":"Optimal discharge of patients from intensive care via a data-driven policy learning framework","authors":"Fernando Lejarza , Jacob Calvert , Misty M. Attwood , Daniel Evans , Qingqing Mao","doi":"10.1016/j.orhc.2023.100400","DOIUrl":"https://doi.org/10.1016/j.orhc.2023.100400","url":null,"abstract":"<div><p>Clinical decision support tools rooted in machine learning and optimization can provide significant value to healthcare providers through better management of intensive care units<span>. In particular, it is important that intensive care unit patient discharge decisions account for the nuanced trade-off between decreasing the length of stay and the risk of readmission or death after discharge<span> of a patient. This work introduces a comprehensive framework (i.e., not geared towards any particular disease or condition) for capturing this trade-off and to recommend optimal discharge timing decisions given the electronic health records of a patient. A data-driven approach is used to derive a parsimonious, discrete state space representation to represent the physiological condition of a given patient. Based on this model and a given cost function, an infinite-horizon discounted Markov decision process is formulated and solved numerically to compute an optimal discharge policy, whose performance is assessed using off-policy evaluation strategies. Extensive numerical experiments are performed to validate the proposed framework using real-life intensive care unit patient data.</span></span></p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"38 ","pages":"Article 100400"},"PeriodicalIF":2.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49842343","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}

引用次数: 2

Innovative Approaches for Emerging Challenges in Health Services and Care (special issue for the 31st European Conference on Operational Research - EURO 2021) 应对卫生服务和护理新挑战的创新方法（第31届欧洲运筹学会议特刊-2021年欧洲版）

IF 2.1

Operations Research for Health Care Pub Date : 2023-09-01 DOI: 10.1016/j.orhc.2023.100388

引用次数: 0

A preventive–reactive approach for nurse scheduling considering absenteeism and nurses’ preferences 考虑缺勤和护士偏好的预防性反应式护士排班方法

IF 2.1

Operations Research for Health Care Pub Date : 2023-09-01 DOI: 10.1016/j.orhc.2023.100389

Ricardo Otero-Caicedo, Carlos Eduardo Montoya Casas, Carolina Barajas Jaimes, Cristian Felipe Guzmán Garzón, Edwin Andrés Yáñez Vergel, Julián Camilo Zabala Valdés

{"title":"A preventive–reactive approach for nurse scheduling considering absenteeism and nurses’ preferences","authors":"Ricardo Otero-Caicedo, Carlos Eduardo Montoya Casas, Carolina Barajas Jaimes, Cristian Felipe Guzmán Garzón, Edwin Andrés Yáñez Vergel, Julián Camilo Zabala Valdés","doi":"10.1016/j.orhc.2023.100389","DOIUrl":"10.1016/j.orhc.2023.100389","url":null,"abstract":"<div><p>The nurse scheduling problem (NSP) has become significant in recent years due to its direct impact on patient healthcare. This problem involves assigning nurses’ shifts while fulfilling a set of hard constraints associated with labor regulations and soft constraints related to personal preferences, workload balance, among others. Most studies have focused on providing solutions for deterministic scenarios without considering unexpected disruptions, such as an unscheduled nurse absence. This study integrates two of the most common approaches to address absenteeism: preventive and reactive. First, we propose a multiobjective linear model for staff scheduling that preventively assigns backup nurses for each day. The NSP is known to be an NP-hard problem. Therefore, we used a genetic algorithm to obtain solutions in a reasonable amount of time. To mitigate the effect of unscheduled nurse absences, we propose two reactive rescheduling policies, one that seeks to maintain the baseline schedule and another that prioritizes the exclusive use of backup nurses. We used Montecarlo simulation under different problem settings to compare the proposed policies with a policy that does not use the preventive approach. The probability that a nurse will accept an additional shift to cover an absence was also considered. Simulation results suggest that both of our preventive–reactive policies outperform the non-preventive policy, especially in the presence of a small probability that a nurse will accept an additional shift. Finally, we used the proposed policies to create the monthly nursing schedule in a reference hospital in Bogotá-Colombia.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"38 ","pages":"Article 100389"},"PeriodicalIF":2.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46849656","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}

引用次数: 1

An optimization model for distribution of influenza vaccines through a green healthcare supply chain 通过绿色医疗供应链分配流感疫苗的优化模型

IF 2.1

Operations Research for Health Care Pub Date : 2023-06-01 DOI: 10.1016/j.orhc.2023.100387

Ilya Levner, Avi Herbon

{"title":"An optimization model for distribution of influenza vaccines through a green healthcare supply chain","authors":"Ilya Levner, Avi Herbon","doi":"10.1016/j.orhc.2023.100387","DOIUrl":"10.1016/j.orhc.2023.100387","url":null,"abstract":"<div><p>The objective of this paper is to minimize the total cost of vaccine storage and distribution operations at centralized distribution centers (DCs) and at clinics so that clinics are provided with vaccines in a timely fashion while under resource and environment-protection constraints. A non-linear mathematical programming model is developed to improve the efficiency of large-scale influenza vaccination programs. The suggested model is tested and justified through computational experiments with real-life data from a Clalit HMO influenza vaccination case study. The investments in green (environment-protecting) activities recommended by the optimal plan are smaller than the expected monetary benefits associated with their effects. A possible application of this research is for optimizing vaccination plans for different subpopulations and various HMOs. Our vaccine supply chain model includes the costs of disposal, recycling, and utilizing clean technologies (i.e., low-pollution gas heating/cooling, electric transportation cars, energy saving policies). It integrates the operational cost/benefit parameters of vaccination programs with the costs/benefits of green activities.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"37 ","pages":"Article 100387"},"PeriodicalIF":2.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48521290","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}

引用次数: 0

Analyzing the relationship between physicians’ experience and surgery duration 医师经验与手术时间的关系分析

IF 2.1

Operations Research for Health Care Pub Date : 2023-03-01 DOI: 10.1016/j.orhc.2022.100377

Oliver Buchholz , Christopher Haager , Katja Schimmelpfeng , Jens O. Brunner , Jan Schoenfelder

{"title":"Analyzing the relationship between physicians’ experience and surgery duration","authors":"Oliver Buchholz , Christopher Haager , Katja Schimmelpfeng , Jens O. Brunner , Jan Schoenfelder","doi":"10.1016/j.orhc.2022.100377","DOIUrl":"10.1016/j.orhc.2022.100377","url":null,"abstract":"<div><p>To construct good quality plans or planning systems in hospitals, such as capacity planning, case mix planning, master surgery scheduling, and shift scheduling, knowing details about the duration of surgeries is paramount. Furthermore, the operating room is one of a hospital’s main cost drivers, thus making surgery duration a key to achieving cost effectiveness. To gain a better understanding of the interdependencies of determining surgery durations, we investigate the influence physicians have on the duration of a surgery. Since physician experience is a very generalizable factor across a heterogeneous group of hospitals, it is the most obvious influencing factor to analyze. Accordingly, we utilize information regarding a physician’s level of experience and examine its impact on surgery durations using data from a German hospital. Although we are forced to use aggregate data for privacy and labor law reasons, a combination of linear and quantile regression analysis allows us to derive several important insights. First, on average, an increase in a physician’s experience leads to a decrease in the duration of a surgery. Second, the effect of the first insight depends on the composition of the surgical team and diminishes in the case of teaching activities. Third, the relationship between experience level and surgery duration varies across the distribution of durations, i.e., the relationship is strongest for short surgeries and weakens as the duration of a surgery increases.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"36 ","pages":"Article 100377"},"PeriodicalIF":2.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42295873","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}

引用次数: 0

Penalty and incentive modeling for hospital readmission reduction 减少再入院的奖惩模型

IF 2.1

Operations Research for Health Care Pub Date : 2023-03-01 DOI: 10.1016/j.orhc.2022.100376

Michelle Alvarado , Behshad Lahijanian , Yi Zhang , Mark Lawley

{"title":"Penalty and incentive modeling for hospital readmission reduction","authors":"Michelle Alvarado , Behshad Lahijanian , Yi Zhang , Mark Lawley","doi":"10.1016/j.orhc.2022.100376","DOIUrl":"10.1016/j.orhc.2022.100376","url":null,"abstract":"<div><p>Nearly 20% of patients are readmitted to hospitals within a specific time period after hospital discharge. High readmission rates place an unnecessary burden on the healthcare system, and new initiatives to reduce preventable hospital readmissions have been established. The United States Hospital Readmission Reduction Program (HRRP) is an example of a health policy reform that links insurance payments to quality of care. Critics of HRRP believe that its punitive mechanism design provides less money to struggling hospitals and, in some cases, fails to provide proper incentives and resources for quality care improvements. An asymmetric penalty-incentive model for hospital readmission reductions was developed and studied for an insurer-led reimbursement system. We formulate a game-theoretic setting involving an insurer and a hospital. We derive the insurer’s optimal policy design and the hospital’s best response in an insurer-led Stackelberg setting with rational agents. The model was analyzed for centralized and decentralized solutions and compared to the do-nothing solution. Most notably, we found that a positive incentive level is necessary for a win-win region to exist. An example from public hospital data for acute myocardial infarction showed that transitioning from the current 3% penalty-only policy to the optimal 5.47% incentive-only policy would result in only a 0.17% increase in insurer costs while inspiring hospitals to maximize level of care and increase hospital profits by 39.7%.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"36 ","pages":"Article 100376"},"PeriodicalIF":2.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42574674","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}

引用次数: 0