Health Care Management Science最新文献_第6页

Process mining to discover patterns in patient outcomes in a Psychological Therapies Service. 过程挖掘以发现心理治疗服务中患者结果的模式。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-05-16 DOI: 10.1007/s10729-023-09641-8

C Potts, R R Bond, J-A Jordan, M D Mulvenna, K Dyer, A Moorhead, A Elliott

{"title":"Process mining to discover patterns in patient outcomes in a Psychological Therapies Service.","authors":"C Potts, R R Bond, J-A Jordan, M D Mulvenna, K Dyer, A Moorhead, A Elliott","doi":"10.1007/s10729-023-09641-8","DOIUrl":"10.1007/s10729-023-09641-8","url":null,"abstract":"In the mental health sector, Psychological Therapies face numerous challenges including ambiguities over the client and service factors that are linked to unfavourable outcomes. Better understanding of these factors can contribute to effective and efficient use of resources within the Service. In this study, process mining was applied to data from the Northern Health and Social Care Trust Psychological Therapies Service (NHSCT PTS). The aim was to explore how psychological distress severity pre-therapy and attendance factors relate to outcomes and how clinicians can use that information to improve the service. Data included therapy episodes (N = 2,933) from the NHSCT PTS for adults with a range of mental health difficulties. Data were analysed using Define-Measure-Analyse model with process mining. Results found that around 11% of clients had pre-therapy psychological distress scores below the clinical cut-off and thus these individuals were unlikely to significantly improve. Clients with fewer cancelled or missed appointments were more likely to significantly improve post-therapy. Pre-therapy psychological distress scores could be a useful factor to consider at assessment for estimating therapy duration, as those with higher scores typically require more sessions. This study concludes that process mining is useful in health services such as NHSCT PTS to provide information to inform caseload planning, service management and resource allocation, with the potential to improve client's health outcomes.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"461-476"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186289/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10602177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption. 一种基于强化学习的优化控制方法，用于管理疫情中断后的择期手术积压。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-04-21 DOI: 10.1007/s10729-023-09636-5

Huyang Xu, Yuanchen Fang, Chun-An Chou, Nasser Fard, Li Luo

{"title":"A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption.","authors":"Huyang Xu, Yuanchen Fang, Chun-An Chou, Nasser Fard, Li Luo","doi":"10.1007/s10729-023-09636-5","DOIUrl":"10.1007/s10729-023-09636-5","url":null,"abstract":"Contagious disease pandemics, such as COVID-19, can cause hospitals around the world to delay nonemergent elective surgeries, which results in a large surgery backlog. To develop an operational solution for providing patients timely surgical care with limited health care resources, this study proposes a stochastic control process-based method that helps hospitals make operational recovery plans to clear their surgery backlog and restore surgical activity safely. The elective surgery backlog recovery process is modeled by a general discrete-time queueing network system, which is formulated by a Markov decision process. A scheduling optimization algorithm based on the piecewise decaying [Formula: see text]-greedy reinforcement learning algorithm is proposed to make dynamic daily surgery scheduling plans considering newly arrived patients, waiting time and clinical urgency. The proposed method is tested through a set of simulated dataset, and implemented on an elective surgery backlog that built up in one large general hospital in China after the outbreak of COVID-19. The results show that, compared with the current policy, the proposed method can effectively and rapidly clear the surgery backlog caused by a pandemic while ensuring that all patients receive timely surgical care. These results encourage the wider adoption of the proposed method to manage surgery scheduling during all phases of a public health crisis.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"430-446"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10119544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10178435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Predicting drug shortages using pharmacy data and machine learning. 使用药房数据和机器学习预测药品短缺。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-03-13 DOI: 10.1007/s10729-022-09627-y

Raman Pall, Yvan Gauthier, Sofia Auer, Walid Mowaswes

{"title":"Predicting drug shortages using pharmacy data and machine learning.","authors":"Raman Pall, Yvan Gauthier, Sofia Auer, Walid Mowaswes","doi":"10.1007/s10729-022-09627-y","DOIUrl":"10.1007/s10729-022-09627-y","url":null,"abstract":"Drug shortages are a global and complex issue having negative impacts on patients, pharmacists, and the broader health care system. Using sales data from 22 Canadian pharmacies and historical drug shortage data, we built machine learning models predicting shortages for the majority of the drugs in the most-dispensed interchangeable groups in Canada. When breaking drug shortages into four classes (none, low, medium, high), we were able to correctly predict the shortage class with 69% accuracy and a kappa value of 0.44, one month in advance, without access to any inventory data from drug manufacturers and suppliers. We also predicted 59% of the shortages deemed to be most impactful (given the demand for the drugs and the potential lack of interchangeable options). The models consider many variables, including the average days of a drug supply per patient, the total days of a drug supply, previous shortages, and the hierarchy of drugs within different drug groups and therapeutic classes. Once in production, the models will allow pharmacists to optimize their orders and inventories, and ultimately reduce the impact of drug shortages on their patients and operations.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"395-411"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10009839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10190347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization models for patient and technician scheduling in hemodialysis centers. 血液透析中心患者和技术人员调度的优化模型。

IF 3.6 3区医学

Health Care Management Science Pub Date : 2023-09-01 Epub Date: 2023-07-03 DOI: 10.1007/s10729-023-09642-7

Farbod Farhadi, Sina Ansari, Francisco Jara-Moroni

{"title":"Optimization models for patient and technician scheduling in hemodialysis centers.","authors":"Farbod Farhadi, Sina Ansari, Francisco Jara-Moroni","doi":"10.1007/s10729-023-09642-7","DOIUrl":"10.1007/s10729-023-09642-7","url":null,"abstract":"Patient and technician scheduling problem in hemodialysis centers presents a unique setting in healthcare operations as (1) unlike other healthcare problems, dialysis appointments have a steady state and the treatment times are determined in advance of the appointments, and (2) once the appointments are set, technicians will have to be assigned to two types of jobs per appointment: putting on and taking off patients (connecting to and disconnecting from dialysis machines). In this study, we design a mixed-integer programming model to minimize technicians' operating costs (regular and overtime costs) at large-scale hemodialysis centers. As this formulation proves to be computationally challenging to solve, we propose a novel reformulation of the problem as a discrete-time assignment model and prove that the two formulations are equivalent under a specific condition. We then simulate instances based on the data from our collaborating hemodialysis center to evaluate the performance of our proposed formulations. We compare our results to the current scheduling policy at the center. In our numerical analysis, we reduced the technician operating costs by 17% on average (up to 49%) compared to the current practice. We further conduct a post-optimality analysis and develop a predictive model that can estimate the number of required technicians based on the center's attributes and patients' input variables. Our predictive model reveals that the optimal number of technicians is strongly related to the time flexibility of patients and their dialysis times. Our findings can help clinic managers at hemodialysis centers to accurately estimate the technician requirements.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"558-582"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10177950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 3.6 3区医学

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

Timo Kuosmanen, Yong Tan, Sheng Dai

{"title":"Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic.","authors":"Timo Kuosmanen, Yong Tan, Sheng Dai","doi":"10.1007/s10729-023-09634-7","DOIUrl":"10.1007/s10729-023-09634-7","url":null,"abstract":"The coronavirus infection COVID-19 killed millions of people around the world in 2019-2022. Hospitals were in the forefront in the battle against the pandemic. This paper proposes a novel approach to assess the effectiveness of hospitals in saving lives. We empirically estimate the production function of COVID-19 deaths among hospital inpatients, applying Heckman's two-stage approach to correct for the bias caused by a large number of zero-valued observations. We subsequently assess performance of hospitals based on regression residuals, incorporating contextual variables to convex quantile regression. Data of 187 hospitals in England over a 35-week period from April to December 2020 is divided in two sub-periods to compare the structural differences between the first and second waves of the pandemic. The results indicate significant performance improvement during the first wave, however, learning by doing was offset by the new mutated virus straits during the second wave. While the elderly patients were at significantly higher risk during the first wave, their expected mortality rate did not significantly differ from that of the general population during the second wave. Our most important empirical finding concerns large and systematic performance differences between individual hospitals: larger units proved more effective in saving lives, and hospitals in London had a lower mortality rate than the national average.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"447-460"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10195617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Managing surgical waiting lists through dynamic priority scoring. 通过动态优先级评分管理手术等待名单。

IF 3.6 3区医学

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

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

{"title":"Managing surgical waiting lists through dynamic priority scoring.","authors":"Jack Powers, James M McGree, David Grieve, Ratna Aseervatham, Suzanne Ryan, Paul Corry","doi":"10.1007/s10729-023-09648-1","DOIUrl":"10.1007/s10729-023-09648-1","url":null,"abstract":"Prioritising elective surgery patients under the Australian three-category system is inherently subjective due to variability in clinician decision making and the potential for extraneous factors to influence category assignment. As a result, waiting time inequities can exist which may lead to adverse health outcomes and increased morbidity, especially for patients deemed to be low priority. This study investigated the use of a dynamic priority scoring (DPS) system to rank elective surgery patients more equitably, based on a combination of waiting time and clinical factors. Such a system enables patients to progress on the waiting list in a more objective and transparent manner, at a rate relative to their clinical need. Simulation results comparing the two systems indicate that the DPS system has potential to assist in managing waiting lists by standardising waiting times relative to urgency category, in addition to improving waiting time consistency for patients of similar clinical need. In clinical practice, this system is likely to reduce subjectivity, increase transparency, and improve overall efficiency of waiting list management by providing an objective metric to prioritise patients. Such a system is also likely to increase public trust and confidence in the systems used to manage waiting lists.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"533-557"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10249174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 2.3 3区医学

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

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

{"title":"Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways.","authors":"Christina C Bartenschlager, Milena Grieger, Johanna Erber, Tobias Neidel, Stefan Borgmann, Jörg J Vehreschild, Markus Steinbrecher, Siegbert Rieg, Melanie Stecher, Christine Dhillon, Maria M Ruethrich, Carolin E M Jakob, Martin Hower, Axel R Heller, Maria Vehreschild, Christoph Wyen, Helmut Messmann, Christiane Piepel, Jens O Brunner, Frank Hanses, Christoph Römmele","doi":"10.1007/s10729-023-09647-2","DOIUrl":"10.1007/s10729-023-09647-2","url":null,"abstract":"The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"412-429"},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10485125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10249635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 3.6 3区医学

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

Rawan Shabbar, Hiroki Sayama

{"title":"Health information exchange network under collaboration, cooperation, and competition: A game-theoretic approach.","authors":"Rawan Shabbar, Hiroki Sayama","doi":"10.1007/s10729-023-09640-9","DOIUrl":"10.1007/s10729-023-09640-9","url":null,"abstract":"Health Information Exchange (HIE) network allows securely accessing and sharing healthcare-related information among healthcare providers (HCPs) and payers. HIE services are provided by a non-profit/profit organizations under several subscription plans options. A few studies have addressed the sustainability of the HIE network such that HIE providers, HCPs, and payers remain profitable in the long term. However, none of these studies addressed the coexistence of multiple HIE providers in the network. Such coexistence may have a huge impact on the behavior of healthcare systems in terms of adoption rate and HIE pricing strategies. In addition, in spite of all the effort to maintain cooperation between HIE providers, there is still a chance of competition among them in the market. Possible competition among service providers leads to many concerns about the HIE network sustainability and behavior. In this study, a game-theoretic approach to model the HIE market is proposed. Game-theory is used to simulate the behavior of the three different HIE network agents in the HIE market: HIE providers, HCPs, and payers. Pricing strategies and adoption decisions are optimized using a Linear Programming (LP) mathematical model. Results show that the relation between HIEs in the market is crucial to HCP/Payer adoption decision specially to small HCPs. A small change in the discount rate proposed by a competitive HIE provider will highly affect the decision of HCP/payers to join the HIE network. Finally, competition opened the opportunity for more HCPs to join the network due to reduced pricing. Furthermore, collaborative HIEs provided better performance compared to cooperative in terms of profit and HCP adoption rate by sharing their overall costs and revenues.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"516-532"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10602700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 2.3 3区医学

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

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

{"title":"Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions.","authors":"Michael R Johnson, Hiten Naik, Wei Siang Chan, Jesse Greiner, Matt Michaleski, Dong Liu, Bruno Silvestre, Ian P McCarthy","doi":"10.1007/s10729-023-09639-2","DOIUrl":"10.1007/s10729-023-09639-2","url":null,"abstract":"During the COVID-19 pandemic, there has been considerable research on how regional and country-level forecasting can be used to anticipate required hospital resources. We add to and build on this work by focusing on ward-level forecasting and planning tools for hospital staff during the pandemic. We present an assessment, validation, and deployment of a working prototype forecasting tool used within a modified Traffic Control Bundling (TCB) protocol for resource planning during the pandemic. We compare statistical and machine learning forecasting methods and their accuracy at one of the largest hospitals (Vancouver General Hospital) in Canada against a medium-sized hospital (St. Paul's Hospital) in Vancouver, Canada through the first three waves of the COVID-19 pandemic in the province of British Columbia. Our results confirm that traditional statistical and machine learning (ML) forecasting methods can provide valuable ward-level forecasting to aid in decision-making for pandemic resource planning. Using point forecasts with upper 95% prediction intervals, such forecasting methods would have provided better accuracy in anticipating required beds on COVID-19 hospital units than ward-level capacity decisions made by hospital staff. We have integrated our methodology into a publicly available online tool that operationalizes ward-level forecasting to aid with capacity planning decisions. Importantly, hospital staff can use this tool to translate forecasts into better patient care, less burnout, and improved planning for all hospital resources during pandemics.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"477-500"},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10192806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 3.6 3区医学

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

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

{"title":"Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients.","authors":"Aleida Braaksma, Martin S Copenhaver, Ana C Zenteno, Elizabeth Ugarph, Retsef Levi, Bethany J Daily, Benjamin Orcutt, Kathryn M Turcotte, Peter F Dunn","doi":"10.1007/s10729-023-09638-3","DOIUrl":"10.1007/s10729-023-09638-3","url":null,"abstract":"Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients' arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches-beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 3","pages":"501-515"},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10174583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0