A decision support system for demand and capacity modelling of an accident and emergency department

© 2019 Operational Research Society.Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 – January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.Peer reviewe

Utilization of big data to improve management of the emergency departments. Results of a systematic review

Background. The emphasis on using big data is growing exponentially in several sectors including biomedicine, life sciences and scientific research, mainly due to advances in information technologies and data analysis techniques. Actually, medical sciences can rely on a large amount of biomedical information and Big Data can aggregate information around multiple scales, from the DNA to the ecosystems. Given these premises, we wondered if big data could be useful to analyze complex systems such as the Emergency Departments (EDs) to improve their management and eventually patient outcomes. Methods. We performed a systematic review of the literature to identify the studies that implemented the application of big data in EDs and to describe what have already been done and what are the expectations, issues and challenges in this field. Results. Globally, eight studies met our inclusion criteria concerning three main activities: the management of ED visits, the ED process and activities and, finally, the prediction of the outcome of ED patients. Although the results of the studies show good perspectives regarding the use of big data in the management of emergency departments, there are still some issues that make their use still difficult. Most of the predictive models and algorithms have been applied only in retrospective studies, not considering the challenge and the costs of a real-time use of big data. Only few studies highlight the possible usefulness of the large volume of clinical data stored into electronic health records to generate evidence in real time. Conclusion. The proper use of big data in this field still requires a better management information flow to allow real-time application

Improving Patient Flow & Reducing Emergency Department (ED) Crowding

Offers early lessons from RWJF's Urgent Matters Learning Network II, a six-hospital collaborative to assess the implementation of strategies for better patient flow and less crowding, develop standard performance measurements, and promote best practices

Impact of Scribes with Flow Coordination Duties on Throughput in an Academic Emergency Department

Introduction: With the increasing influence of electronic health records in emergency medicine came concerns of decreasing operational efficiencies. Particularly worrisome was increasing patient length of stay (LOS). Medical scribes were identified to be in a good position to quickly address barriers to treatment delivery and patient flow. The objective of this study was to investigate patient LOS in the mid- and low-acuity zones of an academic emergency department (ED) with and without medical scribes.Methods: A retrospective cohort study compared patient volume and average LOS between a cohort without scribes and a cohort after the implementation of a scribe-flow coordinator program. Patients were triaged to the mid-acuity Vertical Zone (primarily Emergency Severity Index [ESI] 3) or low-acuity Fast Track (primarily ESI 4 and 5) at a tertiary academic ED. Patients were stratified by treatment zone, acuity level, and disposition.Results: The pre-intervention and post-intervention periods included 8900 patients and 9935 patients, respectively. LOS for patients discharged from the Vertical Zone decreased by 12 minutes from 235 to 223 minutes (p<0.0001, 95% confidence interval [CI], -17,-7) despite a 10% increase in patient volume. For patients admitted from the Vertical Zone, volume increased 13% and LOS remained almost the same, increasing from 225 to 228 minutes (p=0.532, 95% CI, -6,12). For patients discharged from the Fast Track, volume increased 14% and LOS increased six minutes, from 89 to 95 minutes (p<0.0001, 95% CI, 4,9). Predictably, only 1% of Fast Track patients were admitted.Conclusion: Despite substantially increased volume, the use of scribes as patient flow facilitators in the mid-acuity zone was associated with decreased LOS. In the low-acuity zone, scribes were not shown to be as effective, perhaps because rapid patient turnover required them to focus on documentation

Using queuing theory to analyse completion times in accident and emergency departments in the light of the government 4-hour target

This paper uses a queuing model to evaluate completion times in accident and emergency (A&E) departments in the light of the Government target of completing and discharging 98% of patients inside 4 hours. It illustrates how flows though an A&E can be very accurately represented as a queuing process, how the outputs of a queuing model can be used to visualise and interpret the 4-hour hours Government target in a simple way and how queuing models can be used to assess the practical achievability of A&E targets in the future. The paper finds that A&E targets have resulted in significant improvements in completion times and thus deal with a major source of complaint by users of the National Health Service. It finds that whilst some of this improvement is attributable to better management, some is also due to the way some patients in A&E are designated and therefore counted. It finds for example that the current target would not have been possible without some form of patient re-designation or re-labelling taking place. Further it finds that the current target is so demanding that the integrity of reported performance is open to question and that a different approach is needed. Related incentives and demand management issues resulting from this Government target are also briefly discussed

The emergency observation and assessment ward

A recent development to reduce ED crowding and increase urgent patient admissions is the opening of an Emergency Observation and Assessment Ward (EOA Ward). At these wards urgent patients are temporarily hospitalized until they can be transferred to an inpatient bed. In this paper we present an overflow model to evaluate the effect of employing an EOA Ward on elective and urgent patient admissions

Forecasting daily patient outflow from a ward having no real-time clinical data

OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. RESULTS: Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments