Short and Long term predictions of Hospital emergency department attendances.

OBJECTIVE: Emergency departments in the United Kingdom (UK) experience significant difficulties in achieving the 95% NHS access standard due to unforeseen variations in patient flow. In order to maximize efficiency and minimize clinical risk, better forecasting of patient demand is necessary. The objective is therefore to create a tool that accurately predicts attendance at emergency departments to support optimal planning of human and physical resources. METHODS: Historical attendance data between Jan-2011 - December-2015 from four hospitals were used as a training set to develop and validate a forecasting model. To handle weekday variations, the data was first segmented into each weekday time series and a separate model for each weekday was performed. Seasonality testing was performed, followed by Box-Cox transformations. A modified heuristics based on a fuzzy time series model was then developed and compared with autoregressive integrated moving average and neural networks models using Harvey, Leybourne and Newbold (HLN) test. The time series models were tested in four emergency department sites to assess forecasting accuracy using the root mean square error and mean absolute percentage error. The models were tested for (i) short term prediction (four weeks ahead), using weekday time series; and (ii) long term predictions (four months ahead) using monthly time series. RESULTS: Data analysis revealed that presentations to emergency department and subsequent admissions to hospital were not a purely random process and therefore could be predicted with acceptable accuracy. Prediction accuracy improved as the forecast time intervals became wider (from daily to monthly). For each weekday time series modelling using fuzzy time series, for forecasting daily admissions, the mean absolute percentage error ranged from 2.63% to 4.72% while for monthly time series mean absolute percentage error varied from 2.01%-2.81%. For weekday time series, the mean absolute percentage error for autoregressive integrated moving average and neural network forecasting models ranged from 6.25% to 7.47% and 6.04%-7.42% respectively. The proposed fuzzy time series model proved to have statistically significant performance using Harvey, Leybourne and Newbold (HLN) test. This was explained by variations in attendances in different sites and weekdays. CONCLUSIONS: This paper described a heuristic-based fuzzy logic model for predicting emergency department attendances which could help resource allocation and reduce pressure on busy hospitals. Valid and reproducible prediction tools could be generated from these hospital data. The methodology had an acceptable accuracy over a relatively short time period, and could be used to assist better bed management, staffing and elective surgery scheduling. When compared to other prediction models usually applied for emergency department attendances prediction, the proposed heuristic model had better accuracy.

NightShift simulation to train newly qualified doctors in non-technical skills: a feasibility study.

There is growing evidence of greater rates of morbidity and mortality in hospitals during out-of-hours shifts, which appears to be exacerbated during the period in which newly qualified doctors commence work. In order to combat this issue, an online simulation of a night shift was developed and trialled in order to improve the non-technical skills of newly qualified doctors and, ultimately, improve clinical outcomes. A randomised feasibility trial of the electronic training simulation was performed with medical students (n=30) at the end of their training and in the initial weeks of working at a large teaching hospital. The study showed that participants in the intervention group completed their non-urgent tasks more rapidly than the control group: mean (SD) time to complete a non-urgent task of 85.1 (50.1) versus 157.6 (90.4) minutes, p=0.027. This difference persisted using linear regression analysis, which was undertaken using rota and task volume as independent cofactors (p=0.028). This study shows the potential for simulation technologies to improve non-technical skills.