IIE transactions on healthcare systems engineering最新文献_第4页

Robust distribution-free multivariate CUSUM charts for spatiotemporal biosurveillance in the presence of spatial correlation 存在空间相关性的时空生物监测的鲁棒无分布多元CUSUM图

IIE transactions on healthcare systems engineering Pub Date : 2015-04-03 DOI: 10.1080/19488300.2015.1017674

M. Lee, D. Goldsman, Seong-Hee Kim

引用次数: 10

An activity based approach for measuring a hospital's output units of care 一种基于活动的方法，用于衡量医院的护理产出单位

IIE transactions on healthcare systems engineering Pub Date : 2015-04-03 DOI: 10.1080/19488300.2015.1017675

Sanchoy K. Das, S. Boodhoo

{"title":"An activity based approach for measuring a hospital's output units of care","authors":"Sanchoy K. Das, S. Boodhoo","doi":"10.1080/19488300.2015.1017675","DOIUrl":"https://doi.org/10.1080/19488300.2015.1017675","url":null,"abstract":"Traditionally used units of hospital output have been adjusted patient days (APD) and adjusted discharges, both of which assume patient profiles are equivalent across hospitals. This paper introduces a standardized measure for a hospital output unit of care (HUC) as a function of the direct patient care activities. An HUC is defined as the equivalent resources required to deliver one general medical/surgical inpatient day. The HUC follows a patient activity centric approach, and derives an equivalency parameter for each of the care activities in a hospital. These equivalencies provide a model for the roll-up of all care activities into a unified output measure which inherently captures the patient heterogeneity. The developed HUC measure is comprised of five activity components: (i) case-mix adjusted inpatient days, (ii) intensive care, (iii) nursery, (iv) outpatient care, and (v) ancillary services. Within each component are specific billable activities tracked by MEDPAR, the primary data source for the HUC derivation. Application is demonstrated on a set of 1000+ hospitals, utilizing 320 data points for each hospital. The model was validated by showing a strong linear correlation (R2 = 0.894) to the total direct operating cost of a hospital, relative to the APD correlation (R2 = 0.742). Further, there is significant variation in the HUC/APD ratio (μ = 1.75 and σ = 2.05) confirming that the HUC is not just a scalar of APD. Existing and future hospital productivity studies can readily integrate and leverage the HUC output model.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"5 1","pages":"100 - 89"},"PeriodicalIF":0.0,"publicationDate":"2015-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2015.1017675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60567466","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

Operations research/management contributions to emergency department patient flow optimization: Review and research prospects 运筹学/管理学对急诊科患者流程优化的贡献:回顾与研究展望

IIE transactions on healthcare systems engineering Pub Date : 2015-02-04 DOI: 10.1080/19488300.2015.1017676

S. Saghafian, G. Austin, S. Traub

引用次数: 142

Target times for inpatient discharge scheduling 住院病人出院计划的目标时间

IIE transactions on healthcare systems engineering Pub Date : 2015-01-02 DOI: 10.1080/19488300.2014.993445

T. Matis, J. Farris, M. McAllister, Chad Dunavan, A. Snider

引用次数: 4

A hybrid prediction model for no-shows and cancellations of outpatient appointments 门诊预约未到和取消的混合预测模型

IIE transactions on healthcare systems engineering Pub Date : 2015-01-02 DOI: 10.1080/19488300.2014.993006

A. Alaeddini, Kai Yang, Pamela Reeves, C. Reddy

{"title":"A hybrid prediction model for no-shows and cancellations of outpatient appointments","authors":"A. Alaeddini, Kai Yang, Pamela Reeves, C. Reddy","doi":"10.1080/19488300.2014.993006","DOIUrl":"https://doi.org/10.1080/19488300.2014.993006","url":null,"abstract":"A no-show occurs when a scheduled patient neither keeps nor cancels the appointment. A cancellation happens when individuals contact the clinic and cancel their scheduled appointments. Such disruptions not only cause inconvenience to hospital management, they also have a significant impact on the revenue, cost and resource utilization for almost all of the healthcare systems. In this paper, we develop a hybrid probabilistic model based on multinomial logistic regression and Bayesian inference to predict accurately the probability of no-show and cancellation in real-time. First, a multinomial logistic regression model is built based on the entire population's general social and demographic information to provide initial estimates of no-show and cancellation probabilities. Next, the estimated probabilities from the logistic model are transformed into a bivariate Dirichlet distribution, which is used as the prior distribution of a Bayesian updating mechanism to personalize the initial estimates for each patient based on his/her attendance record. In addition, to further improve the estimates, prior to applying the Bayesian updating mechanism, each appointment in the database is weighted based on its recency, weekday of occurrence, and clinic type. The effectiveness of the proposed approach is demonstrated using healthcare data collected at a medical center. We also discuss the advantages of the proposed hybrid model and describe possible real-world applications.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"5 1","pages":"14 - 32"},"PeriodicalIF":0.0,"publicationDate":"2015-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.993006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60566194","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: 31

Scheduling pick-up and delivery jobs in a hospital to level ergonomic stress 在医院安排接送工作来缓解人体工程学的压力

IIE transactions on healthcare systems engineering Pub Date : 2015-01-02 DOI: 10.1080/19488300.2014.996837

Alexander Fröhlich von Elmbach, N. Boysen, Dirk Briskorn, Sascha Mothes

引用次数: 2

A data mining methodology for predicting early stage Parkinson's disease using non-invasive, high-dimensional gait sensor data. 使用非侵入性高维步态传感器数据预测早期帕金森病的数据挖掘方法。

IIE transactions on healthcare systems engineering Pub Date : 2015-01-01 Epub Date: 2015-11-20 DOI: 10.1080/19488300.2015.1095256

Conrad Tucker, Yixiang Han, Harriet Black Nembhard, Mechelle Lewis, Wang-Chien Lee, Nicholas W Sterling, Xuemei Huang

{"title":"A data mining methodology for predicting early stage Parkinson's disease using non-invasive, high-dimensional gait sensor data.","authors":"Conrad Tucker, Yixiang Han, Harriet Black Nembhard, Mechelle Lewis, Wang-Chien Lee, Nicholas W Sterling, Xuemei Huang","doi":"10.1080/19488300.2015.1095256","DOIUrl":"https://doi.org/10.1080/19488300.2015.1095256","url":null,"abstract":"Parkinson's disease (PD) is the second most common neurological disorder after Alzheimer's disease. Key clinical features of PD are motor-related and are typically assessed by healthcare providers based on qualitative visual inspection of a patient's movement/gait/posture. More advanced diagnostic techniques such as computed tomography scans that measure brain function, can be cost prohibitive and may expose patients to radiation and other harmful effects. To mitigate these challenges, and open a pathway to remote patient-physician assessment, the authors of this work propose a data mining driven methodology that uses low cost, non-invasive sensors to model and predict the presence (or lack therefore) of PD movement abnormalities and model clinical subtypes. The study presented here evaluates the discriminative ability of non-invasive hardware and data mining algorithms to classify PD cases and controls. A 10-fold cross validation approach is used to compare several data mining algorithms in order to determine that which provides the most consistent results when varying the subject gait data. Next, the predictive accuracy of the data mining model is quantified by testing it against unseen data captured from a test pool of subjects. The proposed methodology demonstrates the feasibility of using non-invasive, low cost, hardware and data mining models to monitor the progression of gait features outside of the traditional healthcare facility, which may ultimately lead to earlier diagnosis of emerging neurological diseases.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"5 4","pages":"238-254"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2015.1095256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35915539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

MODELING CHRONIC DISEASE PATIENT FLOWS DIVERTED FROM EMERGENCY DEPARTMENTS TO PATIENT-CENTERED MEDICAL HOMES. 模拟慢性病患者从急诊科转移到以患者为中心的医疗之家。

IIE transactions on healthcare systems engineering Pub Date : 2015-01-01 DOI: 10.1080/19488300.2015.1095824

Rafael Diaz, Joshua Behr, Sameer Kumar, Bruce Britton

{"title":"MODELING CHRONIC DISEASE PATIENT FLOWS DIVERTED FROM EMERGENCY DEPARTMENTS TO PATIENT-CENTERED MEDICAL HOMES.","authors":"Rafael Diaz, Joshua Behr, Sameer Kumar, Bruce Britton","doi":"10.1080/19488300.2015.1095824","DOIUrl":"10.1080/19488300.2015.1095824","url":null,"abstract":"Chronic Disease is defined as a long lasting health condition, which can develop and/or worsen over an extended time, but which can also be controlled. The monetary and budgetary toll due to its persistent nature has become unsustainable and requires pressing actions to limit their incidence and burden. This paper demonstrates the utility of the System Dynamics approach to simulate the behavior of key factors involved in the implementation of chronic disease management. We model the patient flow diversion from emergency departments (ED) to patient-centered medical homes (PCMH), with emphasis on the visit rates, as well as the effect of insurance coverage, in an effort to assure continuity of quality care for Asthma patients at lower costs. The model is used as an evaluative method to identify conditions of a maintained health status through adequate policy planning, in terms of resources and capacity. This approach gives decision makers the ability to track the level of implementation of the intervention and generate knowledge about dynamics between population demands and the intervention effectiveness. The functionality of the model is demonstrated through the consideration of hypothetical scenarios executed using sensitivity analysis.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"5 1","pages":"268-285"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2015.1095824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60569135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

How lapse and slip errors influence head-of-bed angle compliance rates as measured by a portable, wireless data collection system. 移动和滑动误差如何影响床头角顺应率，由便携式无线数据收集系统测量。

IIE transactions on healthcare systems engineering Pub Date : 2015-01-01 Epub Date: 2015-03-16 DOI: 10.1080/19488300.2014.993005

Geb W Thomas, Priyadarshini Pennathur, Derik M Falk, Jon Myers, Brennan Ayres, Philip M Polgreen

{"title":"How lapse and slip errors influence head-of-bed angle compliance rates as measured by a portable, wireless data collection system.","authors":"Geb W Thomas, Priyadarshini Pennathur, Derik M Falk, Jon Myers, Brennan Ayres, Philip M Polgreen","doi":"10.1080/19488300.2014.993005","DOIUrl":"https://doi.org/10.1080/19488300.2014.993005","url":null,"abstract":"The recommended protocols to prevent ventilator-associated pneumonia include keeping ventilated patients' head and upper body elevated to an angle between 30 and 45 degrees. These recommendations are largely based on a study that has been difficult to replicate, because studies that have attempted to replicate the original conditions have failed to achieve the necessary bed angles consistently. This work suggests the possibility that two specific types of human error, slips and lapses, contribute to non-compliant bed angles. A novel device provided 83,655 samples of bed angles over a period of 1579 hours. The bed angle was out of compliance 64.2% of the time analyzed. Slips, the accident of raising the bed to an angle slightly less than the desired angle, accounted for most of the out-of-compliance measurements, or 55.9% of the time analyzed. It appears that stochastic variation in the bed adjustments results in the bed being out of compliance. Interventions should be investigated such as increasing the target angle and providing feedback at the moment the bed is raised to close to, but less than, the target angle.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"5 1","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.993005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40448000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Optimizing service times for a public health emergency using a genetic algorithm: Locating dispensing sites and allocating medical staff 使用遗传算法优化突发公共卫生事件的服务时间:定位配药地点和分配医务人员

IIE transactions on healthcare systems engineering Pub Date : 2014-10-02 DOI: 10.1080/19488300.2014.965394

O. Araz, J. Fowler, Adrian Ramirez Nafarrate

{"title":"Optimizing service times for a public health emergency using a genetic algorithm: Locating dispensing sites and allocating medical staff","authors":"O. Araz, J. Fowler, Adrian Ramirez Nafarrate","doi":"10.1080/19488300.2014.965394","DOIUrl":"https://doi.org/10.1080/19488300.2014.965394","url":null,"abstract":"We formulate a p-median facility location model with a queuing approximation to determine the optimal locations of a given number of dispensing sites (Point of Dispensing-PODs) from a predetermined set of possible locations and the optimal allocation of staff to the selected locations. Specific to an anthrax attack, dispensing operations should be completed in 48 hours to cover all exposed and possibly exposed people. A nonlinear integer programming model is developed and it formulates the problem of determining the optimal locations of facilities with appropriate facility deployment strategies, including the amount of servers with different skills to be allocated to each open facility. The objective of the mathematical model is to minimize the average transportation and waiting times of individuals to receive the required service. The mathematical model has waiting time performance measures approximated with a queuing formula and these waiting times at PODs are incorporated into the p-median facility location model. A genetic algorithm is developed to solve this problem. Our computational results show that appropriate locations of these facilities can significantly decrease the average time for individuals to receive services. Consideration of demographics and allocation of the staff decreases waiting times in PODs and increases the throughput of PODs. When the number of PODs to open is high, the right staffing at each facility decreases the average waiting times significantly. The results presented in this paper can help public health decision makers make better planning and resource allocation decisions based on the demographic needs of the affected population.","PeriodicalId":89563,"journal":{"name":"IIE transactions on healthcare systems engineering","volume":"4 1","pages":"178 - 190"},"PeriodicalIF":0.0,"publicationDate":"2014-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19488300.2014.965394","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60565047","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: 13