Konrad R.,Worcester Polytechnic Institute |
DeSotto K.,New England Veterans Engineering Resource Center |
Grocela A.,Worcester Polytechnic Institute |
McAuley P.,Worcester Polytechnic Institute |
And 3 more authors.
Operations Research for Health Care
We report on the use of discrete-event simulation modeling to support process improvements in a hospital emergency department (ED), namely the implementation of a split-flow process. Our partner hospital was effective in treating patients, but wait time and congestion in the ED created patient dissatisfaction, unsafe conditions and staff morale issues. The split-flow concept is an emerging approach to manage ED processes by splitting patient flow according to patient acuity and enabling parallel processing. We contrast the split-flow operational model to other types of ED triage. While early implementations of the split-flow concept have demonstrated significant improvements in patient wait times, a systematic evaluation of operational configurations is lacking.We created a discrete-event simulation model and established its face validity for Saint Vincent Hospital in Worcester, USA, a community-teaching, Level II Trauma Center. Seventeen scenarios were tested to estimate the likely impact of a split-flow process redesign, including staffing level changes and patient volume changes. The scenarios were compared in terms of Door-to-Doctor time and length-of-stay for different patient acuity levels.Findings from the study supported implementation of the split-flow improvements. Statistical analysis of data taken before and after the implementation indicate that waiting time measures were significantly improved and overall patient length-of-stay was reduced. To gauge the success of the current split-flow process at Saint Vincent we compare performance metrics from three different sources: benchmark metrics, hospital data prior to split-flow implementation, and performance metrics post implementation. © 2013 Elsevier Ltd. Source
Al-Haque S.,Massachusetts Institute of Technology |
Ceyhan M.E.,Massachusetts Institute of Technology |
Chan S.H.,New England Veterans Engineering Resource Center |
Nightingale D.J.,Massachusetts Institute of Technology
The Veterans Health Administration (VHA) provides care to over 8 million Veterans and operates over 1,700 sites of care across 21 regional networks in the United States. Health care providers within VHA report large seasonal variation in the demand for services, especially in the southern United States because of arrival of “snowbirds” during the winter. Because resource allocation activities are primarily carried out through an annual budgeting process, the seasonal load imposed by “traveling Veterans”—Veterans that seek care at VHA sites outside of their home network—make providing high-quality services more challenging. This work constitutes the first major effort within VHA to understand the impact of traveling Veterans. We discovered strong seasonal fluctuations in demand at a clinic located in the southeastern United States and developed a seasonal autoregressive integrated moving average model to help the clinic forecast demand for its services with significantly less error than historical averaging. Monte Carlo simulation of the clinic revealed that physicians are overutilized, suggesting the need to re-evaluate how the clinic is currently staffed. More broadly, this study demonstrates how operations management methods can assist operational decision making at other clinics and medical centers both within and outside VHA. © AMSUS - The society of Federal Health Professionals, 2015 printed in U.S.A. All rights reserved. Source
Kim B.,New England Veterans Engineering Resource Center |
Elstein Y.,New England Veterans Engineering Resource Center |
Shiner B.,New England Veterans Engineering Resource Center |
Shiner B.,White River Junction Medical Center |
And 4 more authors.
General Hospital Psychiatry
Objective: To improve clinic design, trial-and-error is commonly used to discover strategies that lead to improvement. Our goal was to predict the effects of various changes before undertaking them. Method: Systems engineers collaborated with staff at an integrated primary care-mental health care clinic to create a computer simulation that mirrored how the clinic currently operates. We then simulated hypothetical changes to the staffing to understand their effects on percentage of patients seen outside scheduled clinic hours and service completion time. Results: We found that, out of the change options being considered by the clinic, extending daily clinic hours by two and including an additional psychiatrist are likely to result in the greatest incremental decreases in patients seen outside clinic hours and in service time. Conclusion: Simulation in partnership with engineers can be an attractive tool for improving mental health clinics, particularly when changes are costly and thus trial-and-error is not desirable. © 2013. Source
Peck J.S.,New England Veterans Engineering Resource Center |
Peck J.S.,Massachusetts Institute of Technology |
Gaehde S.A.,Emergency Medicine Service |
Nightingale D.J.,Massachusetts Institute of Technology |
And 6 more authors.
Academic Emergency Medicine
Objectives: The objective was to test the generalizability, across a range of hospital sizes and demographics, of a previously developed method for predicting and aggregating, in real time, the probabilities that emergency department (ED) patients will be admitted to a hospital inpatient unit. Methods: Logistic regression models were developed that estimate inpatient admission probabilities of each patient upon entering an ED. The models were based on retrospective development (n = 4,000 to 5,000 ED visits) and validation (n = 1,000 to 2,000 ED visits) data sets from four heterogeneous hospitals. Model performance was evaluated using retrospective test data sets (n = 1,000 to 2,000 ED visits). For one hospital the developed model also was applied prospectively to a test data set (n = 910 ED visits) coded by triage nurses in real time, to compare results to those from the retrospective single investigator-coded test data set. Results: The prediction models for each hospital performed reasonably well and typically involved just a few simple-to-collect variables, which differed for each hospital. Areas under receiver operating characteristic curves (AUC) ranged from 0.80 to 0.89, R2 correlation coefficients between predicted and actual daily admissions ranged from 0.58 to 0.90, and Hosmer-Lemeshow goodness-of-fit statistics of model accuracy had p > 0.01 with one exception. Data coded prospectively by triage nurses produced comparable results. Conclusions: The accuracy of regression models to predict ED patient admission likelihood was shown to be generalizable across hospitals of different sizes, populations, and administrative structures. Each hospital used a unique combination of predictive factors that may reflect these differences. This approach performed equally well when hospital staff coded patient data in real time versus the research team retrospectively. © 2013 by the Society for Academic Emergency Medicine. Source
Shiner B.,Medical Center |
Shiner B.,New England Veterans Engineering Resource Center |
Shiner B.,National Center for |
D'Avolio L.W.,VA Boston Health care System |
And 15 more authors.
Administration and Policy in Mental Health and Mental Health Services Research
To improve methods of estimating use of evidence-based psychotherapy for posttraumatic stress disorder in the Veteran's health administration, we evaluated administrative data and note text for patients newly enrolling in six VHA outpatient PTSD clinics in New England during the 2010 fiscal year (n = 1,924). Using natural language processing, we developed machine learning algorithms that mimic human raters in classifying note text. We met our targets for algorithm performance as measured by precision, recall, and F-measure. We found that 6.3 % of our study population received at least one session of evidence-based psychotherapy during the initial 6 months of treatment. Evidence-based psychotherapies appear to be infrequently utilized in VHA outpatient PTSD clinics in New England. Our method could support efforts to improve use of these treatments. © 2012 Springer Science+Business Media, LLC (outside the USA). Source