Health Care Management Science最新文献

{"title":"Machine learning for optimal test admission in the presence of resource constraints.","authors":"Ramy Elitzur, Dmitry Krass, Eyal Zimlichman","doi":"10.1007/s10729-022-09624-1","DOIUrl":"10.1007/s10729-022-09624-1","url":null,"abstract":"<p><p>Developing rapid tools for early detection of viral infection is crucial for pandemic containment. This is particularly crucial when testing resources are constrained and/or there are significant delays until the test results are available - as was quite common in the early days of Covid-19 pandemic. We show how predictive analytics methods using machine learning algorithms can be combined with optimal pre-test screening mechanisms, greatly increasing test efficiency (i.e., rate of true positives identified per test), as well as to allow doctors to initiate treatment before the test results are available. Our optimal test admission policies account for imperfect accuracy of both the medical test and the model prediction mechanism. We derive the accuracy required for the optimized admission policies to be effective. We also show how our policies can be extended to re-testing high-risk patients, as well as combined with pool testing approaches. We illustrate our techniques by applying them to a large data reported by the Israeli Ministry of Health for RT-PCR tests from March to September 2020. Our results demonstrate that in the context of the Covid-19 pandemic a pre-test probability screening tool with conventional RT-PCR testing could have potentially increased efficiency by several times, compared to random admission control.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"279-300"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9669575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A queuing model for ventilator capacity management during the COVID-19 pandemic.","authors":"Samantha L Zimmerman, Alexander R Rutherford, Alexa van der Waall, Monica Norena, Peter Dodek","doi":"10.1007/s10729-023-09632-9","DOIUrl":"10.1007/s10729-023-09632-9","url":null,"abstract":"<p><p>We applied a queuing model to inform ventilator capacity planning during the first wave of the COVID-19 epidemic in the province of British Columbia (BC), Canada. The core of our framework is a multi-class Erlang loss model that represents ventilator use by both COVID-19 and non-COVID-19 patients. Input for the model includes COVID-19 case projections, and our analysis incorporates projections with different levels of transmission due to public health measures and social distancing. We incorporated data from the BC Intensive Care Unit Database to calibrate and validate the model. Using discrete event simulation, we projected ventilator access, including when capacity would be reached and how many patients would be unable to access a ventilator. Simulation results were compared with three numerical approximation methods, namely pointwise stationary approximation, modified offered load, and fixed point approximation. Using this comparison, we developed a hybrid optimization approach to efficiently identify required ventilator capacity to meet access targets. Model projections demonstrate that public health measures and social distancing potentially averted up to 50 deaths per day in BC, by ensuring that ventilator capacity was not reached during the first wave of COVID-19. Without these measures, an additional 173 ventilators would have been required to ensure that at least 95% of patients can access a ventilator immediately. Our model enables policy makers to estimate critical care utilization based on epidemic projections with different transmission levels, thereby providing a tool to quantify the interplay between public health measures, necessary critical care resources, and patient access indicators.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"200-216"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10201510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9990362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operating room design using agent-based simulation to reduce room obstructions.","authors":"Kevin Taaffe,&nbsp;Yann B Ferrand,&nbsp;Amin Khoshkenar,&nbsp;Lawrence Fredendall,&nbsp;Dee San,&nbsp;Patrick Rosopa,&nbsp;Anjali Joseph","doi":"10.1007/s10729-022-09622-3","DOIUrl":"https://doi.org/10.1007/s10729-022-09622-3","url":null,"abstract":"<p><p>This study seeks to improve the safety of clinical care provided in operating rooms (OR) by examining how characteristics of both the physical environment and the procedure affect surgical team movement and contacts. We video recorded staff movements during a set of surgical procedures. Then we divided the OR into multiple zones and analyzed the frequency and duration of movement from origin to destination through zones. This data was abstracted into a generalized, agent-based, discrete event simulation model to study how OR size and OR equipment layout affected surgical staff movement and total number of surgical team contacts during a procedure. A full factorial experiment with seven input factors - OR size, OR shape, operating table orientation, circulating nurse (CN) workstation location, team size, number of doors, and procedure type - was conducted. Results were analyzed using multiple linear regression with surgical team contacts as the dependent variable. The OR size, the CN workstation location, and team size significantly affected surgical team contacts. Also, two- and three-way interactions between staff, procedure type, table orientation, and CN workstation location significantly affected contacts. We discuss implications of these findings for OR managers and for future research about designing future ORs.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"261-278"},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369668/pdf/nihms-1909061.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10249264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating simple classification rules to predict local surges in COVID-19 hospitalizations.","authors":"Reza Yaesoubi, Shiying You, Qin Xi, Nicolas A Menzies, Ashleigh Tuite, Yonatan H Grad, Joshua A Salomon","doi":"10.1007/s10729-023-09629-4","DOIUrl":"10.1007/s10729-023-09629-4","url":null,"abstract":"<p><p>Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"301-312"},"PeriodicalIF":2.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9975996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Priority-based replenishment policy for robotic dispensing in central fill pharmacy systems: a simulation-based study.","authors":"Nieqing Cao,&nbsp;Austin Marcus,&nbsp;Lubna Altarawneh,&nbsp;Soongeol Kwon","doi":"10.1007/s10729-023-09630-x","DOIUrl":"https://doi.org/10.1007/s10729-023-09630-x","url":null,"abstract":"<p><p>In recent years, companies that operate pharmacy store chains have adopted centralized and automated fulfillment systems, which are called Central Fill Pharmacy Systems (CFPS). The Robotic Dispensing System (RDS) plays a crucial role by automatically storing, counting, and dispensing various medication pills to enable CFPS to fulfill high-volume prescriptions safely and efficiently. Although the RDS is highly automated by robots and software, medication pills in the RDS should still be replenished by operators in a timely manner to prevent the shortage of medication pills that causes huge delays in prescription fulfillment. Because the complex dynamics of the CFPS and manned operations are closely associated with the RDS replenishment process, there is a need for systematic approaches to developing a proper replenishment control policy. This study proposes an improved priority-based replenishment policy, which is able to generate a real-time replenishment sequence for the RDS. In particular, the policy is based on a novel criticality function calculating the refilling urgency for a canister and corresponding dispenser, which takes the inventory level and consumption rates of medication pills into account. A 3D discrete-event simulation is developed to emulate the RDS operations in the CFPS to evaluate the proposed policy based on various measurements numerically. The numerical experiment shows that the proposed priority-based replenishment policy can be easily implemented to enhance the RDS replenishment process by preventing over 90% of machine inventory shortages and saving nearly 80% product fulfillment delays.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"344-362"},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10008078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9988803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting no-show appointments in a pediatric hospital in Chile using machine learning.","authors":"J Dunstan,&nbsp;F Villena,&nbsp;J P Hoyos,&nbsp;V Riquelme,&nbsp;M Royer,&nbsp;H Ramírez,&nbsp;J Peypouquet","doi":"10.1007/s10729-022-09626-z","DOIUrl":"https://doi.org/10.1007/s10729-022-09626-z","url":null,"abstract":"<p><p>The Chilean public health system serves 74% of the country's population, and 19% of medical appointments are missed on average because of no-shows. The national goal is 15%, which coincides with the average no-show rate reported in the private healthcare system. Our case study, Doctor Luis Calvo Mackenna Hospital, is a public high-complexity pediatric hospital and teaching center in Santiago, Chile. Historically, it has had high no-show rates, up to 29% in certain medical specialties. Using machine learning algorithms to predict no-shows of pediatric patients in terms of demographic, social, and historical variables. To propose and evaluate metrics to assess these models, accounting for the cost-effective impact of possible intervention strategies to reduce no-shows. We analyze the relationship between a no-show and demographic, social, and historical variables, between 2015 and 2018, through the following traditional machine learning algorithms: Random Forest, Logistic Regression, Support Vector Machines, AdaBoost and algorithms to alleviate the problem of class imbalance, such as RUS Boost, Balanced Random Forest, Balanced Bagging and Easy Ensemble. These class imbalances arise from the relatively low number of no-shows to the total number of appointments. Instead of the default thresholds used by each method, we computed alternative ones via the minimization of a weighted average of type I and II errors based on cost-effectiveness criteria. 20.4% of the 395,963 appointments considered presented no-shows, with ophthalmology showing the highest rate among specialties at 29.1%. Patients in the most deprived socioeconomic group according to their insurance type and commune of residence and those in their second infancy had the highest no-show rate. The history of non-attendance is strongly related to future no-shows. An 8-week experimental design measured a decrease in no-shows of 10.3 percentage points when using our reminder strategy compared to a control group. Among the variables analyzed, those related to patients' historical behavior, the reservation delay from the creation of the appointment, and variables that can be associated with the most disadvantaged socioeconomic group, are the most relevant to predict a no-show. Moreover, the introduction of new cost-effective metrics significantly impacts the validity of our prediction models. Using a prototype to call patients with the highest risk of no-shows resulted in a noticeable decrease in the overall no-show rate.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"313-329"},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9607622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance measurement of nonhomogeneous Hong Kong hospitals using directional distance functions.","authors":"Shuguang Lin,&nbsp;Paul Rouse,&nbsp;Ying-Ming Wang,&nbsp;Lin Lin,&nbsp;Zhen-Quan Zheng","doi":"10.1007/s10729-022-09625-0","DOIUrl":"https://doi.org/10.1007/s10729-022-09625-0","url":null,"abstract":"<p><p>Cook et al. (Oper Res 61(3):666-676, 2013) propose a DEA-based model for the performance evaluation of non-homogeneous decision making units (DMUs) based on constant returns to scale (CRS), extended by Li et al. (Health Care Manag Sci 22(2):215-228, 2019) to variable returns to scale (VRS). This paper locates these models into more general DDF models to deal with nonhomogeneous DMUs and applies these to Hong Kong hospitals. The production process of each hospital is divided into subunits which have the same inputs and outputs and hospital performance is measured using the subunits. The paper provides CRS and VRS versions of DDF models and compares them with Cook et al. (Oper Res 61(3):666-676, 2013) and Li et al. (Health Care Manag Sci 22(2):215-228, 2019). A kernel-based method is used to estimate the distributions as well as a DEA-based efficiency analysis adapted by Simar and Zelenyuk to test the distributions. Both DDF CRS and VRS versions produce results similar to Cook et al. (Oper Res 61(3):666-676, 2013) and Li et al. (Health Care Manag Sci 22(2):215-228, 2019) respectively. However, the statistical tests find differences for the different technologies assumed as would be expected. For hospital managers, the more generalised DDF models expand their range of options in terms of directional improvements and priorities as well as dealing with non-homogeneity.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"330-343"},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9607629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Who should see the patient? on deviations from preferred patient-provider assignments in hospitals.","authors":"Mariam K Atkinson,&nbsp;Soroush Saghafian","doi":"10.1007/s10729-022-09628-x","DOIUrl":"https://doi.org/10.1007/s10729-022-09628-x","url":null,"abstract":"<p><p>In various organizations including hospitals, individuals are not forced to follow specific assignments, and thus, deviations from preferred task assignments are common. This is due to the conventional wisdom that professionals should be given the flexibility to deviate from preferred assignments as needed. It is unclear, however, whether and when this conventional wisdom is true. We use evidence on the assignments of generalist and specialists to patients in our partner hospital (a children's hospital), and generate insights into whether and when hospital administrators should disallow such flexibility. We do so by identifying 73 top medical diagnoses and using detailed patient-level electronic medical record (EMR) data of more than 4,700 hospitalizations. In parallel, we conduct a survey of medical experts and utilized it to identify the preferred provider type that should have been assigned to each patient. Using these two sources of data, we examine the consequence of deviations from preferred provider assignments on three sets of performance measures: operational efficiency (measured by length of stay), quality of care (measured by 30-day readmissions and adverse events), and cost (measured by total charges). We find that deviating from preferred assignments is beneficial for task types (patients' diagnosis in our setting) that are either (a) well-defined (improving operational efficiency and costs), or (b) require high contact (improving costs and adverse events, though at the expense of lower operational efficiency). For other task types (e.g., highly complex or resource-intensive tasks), we observe that deviations are either detrimental or yield no tangible benefits, and thus, hospitals should try to eliminate them (e.g., by developing and enforcing assignment guidelines). To understand the causal mechanism behind our results, we make use of mediation analysis and find that utilizing advanced imaging (e.g., MRIs, CT scans, or nuclear radiology) plays an important role in how deviations impact performance outcomes. Our findings also provide evidence for a \"no free lunch\" theorem: while for some task types, deviations are beneficial for certain performance outcomes, they can simultaneously degrade performance in terms of other dimensions. To provide clear recommendations for hospital administrators, we also consider counterfactual scenarios corresponding to imposing the preferred assignments fully or partially, and perform cost-effectiveness analyses. Our results indicate that enforcing the preferred assignments either for all tasks or only for resource-intensive tasks is cost-effective, with the latter being the superior policy. Finally, by comparing deviations during weekdays and weekends, early shifts and late shifts, and high congestion and low congestion periods, our results shed light on some environmental conditions under which deviations occur more in practice.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"165-199"},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9606303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mixed-integer slacks-based measure data envelopment analysis for efficiency measuring of German university hospitals.","authors":"Mansour Zarrin","doi":"10.1007/s10729-022-09620-5","DOIUrl":"https://doi.org/10.1007/s10729-022-09620-5","url":null,"abstract":"<p><p>Standard Data Envelopment Analysis (DEA) models consider continuous-valued and known input and output statuses for measures. This paper proposes an extended Slacks-Based Measure (SBM) DEA model to accommodate flexible (a measure that can play the role of input and output) and integer measures simultaneously. A flexible measure's most appropriate role (designation) is determined by maximizing the technical efficiency of each unit. The main advantage of the proposed model is that all inputs, outputs, and flexible measures can be expressed in integer values without inflation of efficiency scores since they are directly calculated by modifying input and output inefficiencies. Furthermore, we illustrate and examine the application of the proposed models with 28 university hospitals in Germany. We investigate the differences and common properties of the proposed models with the literature to shed light on both teaching and general inefficiencies. Results of inefficiency decomposition indicate that \"Third-party funding income\" that university hospitals receive from the research-granting agencies dominates the other inefficiencies sources. The study of the efficiency scores is then followed up with a second-stage regression analysis based on efficiency scores and environmental factors. The result of the regression analysis confirms the conclusion derived from the inefficiency decomposition analysis.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 1","pages":"138-160"},"PeriodicalIF":3.6,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9244074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring policy in the context of preventive treatment of cardiovascular disease.","authors":"Daniel F Otero-Leon,&nbsp;Mariel S Lavieri,&nbsp;Brian T Denton,&nbsp;Jeremy Sussman,&nbsp;Rodney A Hayward","doi":"10.1007/s10729-022-09621-4","DOIUrl":"https://doi.org/10.1007/s10729-022-09621-4","url":null,"abstract":"<p><p>Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. Monitoring too frequently may be unnecessary and costly; on the other hand, monitoring the patient infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We propose a finite horizon and finite-state Markov decision process to define monitoring policies. To build our Markov decision process, we estimate stochastic models based on longitudinal observational data from electronic health records for a large cohort of patients seen in the national U.S. Veterans Affairs health system. We use our model to study policies for whether or when to assess the need for cholesterol-lowering medications. We further use our model to investigate the role of gender and race on optimal monitoring policies.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 1","pages":"93-116"},"PeriodicalIF":3.6,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9106989","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}