Health Care Management Science最新文献

{"title":"Screening for preclinical Alzheimer's disease: Deriving optimal policies using a partially observable Markov model.","authors":"Zehra Önen Dumlu,&nbsp;Serpil Sayın,&nbsp;İbrahim Hakan Gürvit","doi":"10.1007/s10729-022-09608-1","DOIUrl":"https://doi.org/10.1007/s10729-022-09608-1","url":null,"abstract":"<p><p>Alzheimer's Disease (AD) is believed to be the most common type of dementia. Even though screening for AD has been discussed widely, there is no screening program implemented as part of a policy in any country. Current medical research motivates focusing on the preclinical stages of the disease in a modeling initiative. We develop a partially observable Markov decision process model to determine optimal screening programs. The model contains disease free and preclinical AD partially observable states and the screening decision is taken while an individual is in one of those states. An observable diagnosed preclinical AD state is integrated along with observable mild cognitive impairment, AD and death states. Transition probabilities among states are estimated using data from Knight Alzheimer's Disease Research Center (KADRC) and relevant literature. With an objective of maximizing expected total quality-adjusted life years (QALYs), the output of the model is an optimal screening program that specifies at what points in time an individual over 50 years of age with a given risk of AD will be directed to undergo screening. The screening test used to diagnose preclinical AD has a positive disutility, is imperfect and its sensitivity and specificity are estimated using the KADRC data set. We study the impact of a potential intervention with a parameterized effectiveness and disutility on model outcomes for three different risk profiles (low, medium and high). When intervention effectiveness and disutility are at their best, the optimal screening policy is to screen every year between ages 50 and 95, with an overall QALY gain of 0.94, 1.9 and 2.9 for low, medium and high risk profiles, respectively. As intervention effectiveness diminishes and/or its disutility increases, the optimal policy changes to sporadic screening and then to never screening. Under several scenarios, some screening within the time horizon is optimal from a QALY perspective. Moreover, an in-depth analysis of costs reveals that implementing these policies are either cost-saving or cost-effective.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 1","pages":"1-20"},"PeriodicalIF":3.6,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9115122","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":"On the use of partitioning for scheduling of surgeries in the inpatient surgical department.","authors":"Lien Wang,&nbsp;Erik Demeulemeester,&nbsp;Nancy Vansteenkiste,&nbsp;Frank E Rademakers","doi":"10.1007/s10729-022-09598-0","DOIUrl":"https://doi.org/10.1007/s10729-022-09598-0","url":null,"abstract":"<p><p>In hospitals, the efficient planning of the operating rooms (ORs) is difficult due to the uncertainty inherent to surgical services. This is especially true for the inpatient surgical department where complex and long surgeries are often performed along with surgeries on emergency patients. This paper aims to improve the scheduling of the inpatient department by partitioning the elective surgeries into the more predictable surgeries (MPS) group and the less predictable surgeries (LPS) group, based on surgery duration variability, and by scheduling each of the two surgery groups in different ORs. Through a simulation study that comprehensively investigates the impact of the partitioning on different performance measures under various environmental settings, we report important findings and insights. First, partitioning can effectively shorten the waiting times of elective patients for both MPS and LPS groups, but the option should be allowed to reassign patients from the MPS or LPS ORs to the other ORs when needed. Meanwhile, partitioning sometimes slightly increases the elective cancellation rate. Second, the ability to use the available capacity of the ORs as much as possible is key to reducing elective waiting times. Third, partitioning might slightly worsen the waiting times of emergency patients, while the slightly negative impact on emergency patients decreases when the number of ORs is higher. Fourth, the beneficial impact of partitioning on elective patients increases with an increased patient demand. Last, for the settings considered in this study there was no benefit in partitioning the elective patients into more than two groups.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"25 4","pages":"526-550"},"PeriodicalIF":3.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10444701","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":"Quantile regression forests for individualized surgery scheduling.","authors":"Arlen Dean,&nbsp;Amirhossein Meisami,&nbsp;Henry Lam,&nbsp;Mark P Van Oyen,&nbsp;Christopher Stromblad,&nbsp;Nick Kastango","doi":"10.1007/s10729-022-09609-0","DOIUrl":"https://doi.org/10.1007/s10729-022-09609-0","url":null,"abstract":"<p><p>Determining the optimal surgical case start times is a challenging stochastic optimization problem that shares a key feature with many other healthcare operations problems. Namely, successful problem solutions require using a vast array of available historical data to create distributions that accurately capture a case duration's uncertainty for integration into an optimization model. Distribution fitting is the conventional approach to generate these distributions, but it can only employ a limited, aggregate portion of the detailed patient features available in Electronic Medical Records systems today. If all the available information can be taken advantage of, then distributions individualized to every case can be constructed whose precision would support higher quality solutions in the presence of uncertainty. Our individualized stochastic optimization framework shows how the quantile regression forest (QRF) method predicts individualized distributions that are integrable into sample-average approximation, robust optimization, and distributionally robust optimization models for problems like surgery scheduling. In this paper, we present some related theoretical performance guarantees for each formulation. Numerically, we also study our approach's benefits relative to three other traditional models using data from Memorial Sloan Kettering Cancer Center in New York, NY, USA.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"25 4","pages":"682-709"},"PeriodicalIF":3.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9294501","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":"Optimal breast cancer risk reduction policies tailored to personal risk level.","authors":"Mehmet A Ergun,&nbsp;Ali Hajjar,&nbsp;Oguzhan Alagoz,&nbsp;Murtuza Rampurwala","doi":"10.1007/s10729-022-09596-2","DOIUrl":"https://doi.org/10.1007/s10729-022-09596-2","url":null,"abstract":"<p><p>Depending on personal and hereditary factors, each woman has a different risk of developing breast cancer, one of the leading causes of death for women. For women with a high-risk of breast cancer, their risk can be reduced by two main therapeutic approaches: 1) preventive treatments such as hormonal therapies (i.e., tamoxifen, raloxifene, exemestane); or 2) a risk reduction surgery (i.e., mastectomy). Existing national clinical guidelines either fail to incorporate or have limited use of the personal risk of developing breast cancer in their proposed risk reduction strategies. As a result, they do not provide enough resolution on the benefit-risk trade-off of an intervention policy as personal risk changes. In addressing this problem, we develop a discrete-time, finite-horizon Markov decision process (MDP) model with the objective of maximizing the patient's total expected quality-adjusted life years. We find several useful insights some of which contradict the existing national breast cancer risk reduction recommendations. For example, we find that mastectomy is the optimal choice for the border-line high-risk women who are between ages 22 and 38. Additionally, in contrast to the National Comprehensive Cancer Network recommendations, we find that exemestane is a plausible, in fact, the best, option for high-risk postmenopausal women.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"25 3","pages":"363-388"},"PeriodicalIF":3.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445480/pdf/nihms-1911145.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10053555","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 tactical multi-week implicit tour scheduling model with applications in healthcare","authors":"M. Isken, Osman T. Aydas","doi":"10.1007/s10729-022-09601-8","DOIUrl":"https://doi.org/10.1007/s10729-022-09601-8","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"25 1","pages":"551 - 573"},"PeriodicalIF":3.6,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42045335","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":"Beyond patient-sharing: Comparing physician- and patient-induced networks","authors":"Eva Kesternich, Olaf N. Rank","doi":"10.1007/s10729-022-09595-3","DOIUrl":"https://doi.org/10.1007/s10729-022-09595-3","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"25 1","pages":"498 - 514"},"PeriodicalIF":3.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44732005","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":"The importance of peer imitation on smoking initiation over time: a dynamical systems approach.","authors":"Carl Simon, David Mendez","doi":"10.1007/s10729-021-09583-z","DOIUrl":"10.1007/s10729-021-09583-z","url":null,"abstract":"<p><p>A recent Institute of Medicine Report calls for explicit modeling of smoking initiation, cessation and addiction processes. We introduce a model of smoking initiation that explicitly teases out the percentage of initiation due to social pressures, which we call \"peer-imitation,\" and the percentage due to other factors, such as media ads, family smoking, and psychological factors, which we call \"self-initiation.\" We propose a dynamic non-linear behavioral contagion model of smoking initiation and employ data from the National Survey on Drug Use and Health to estimate the relative contributions of imitation and self-initiation to the overall smoking initiation process. Although the percent of total smoking due to peer imitation has been trending downward over time, it remains higher than the percent due to self-initiation. We note unexpected changes for the 2007 cohort, and we discuss possible implications for intervention and for the spread of e-cigarettes.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"25 2","pages":"222-236"},"PeriodicalIF":2.3,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9754948","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":"What is the value of explicit priority setting for health interventions? A simulation study","authors":"E. Barlow, A. Morton, S. Dabak, Sven Engels, W. Isaranuwatchai, Y. Teerawattananon, K. Chalkidou","doi":"10.1007/s10729-022-09594-4","DOIUrl":"https://doi.org/10.1007/s10729-022-09594-4","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"25 1","pages":"460 - 483"},"PeriodicalIF":3.6,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47391908","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 multi-product multi-period stochastic model for a blood supply chain considering blood substitution and demand uncertainty","authors":"Yuan Xu, Joseph G. Szmerekovsky","doi":"10.1007/s10729-022-09593-5","DOIUrl":"https://doi.org/10.1007/s10729-022-09593-5","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"25 1","pages":"441 - 459"},"PeriodicalIF":3.6,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45932906","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 dynamic sequential decision-making model on MRI real-time scheduling with simulation-based optimization","authors":"Bowen Pang, Xiaolei Xie, Feng Ju, J. Pipe","doi":"10.1007/s10729-022-09592-6","DOIUrl":"https://doi.org/10.1007/s10729-022-09592-6","url":null,"abstract":"","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"25 1","pages":"426 - 440"},"PeriodicalIF":3.6,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41384570","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}