Predicting hospital emergency department visits accurately: A systematic review.

OBJECTIVES: The emergency department (ED) is a very important healthcare entrance point, known for its challenging organisation and management due to demand unpredictability. An accurate forecast system of ED visits is crucial to the implementation of better management strategies that optimise resources utilization, reduce costs and improve public confidence. The aim of this review is to investigate the different factors that affect the ED visits forecasting outcomes, in particular the predictive variables and type of models applied. METHODS: A systematic search was conducted in PubMed, Web of Science and Scopus. The review methodology followed the PRISMA statement guidelines. RESULTS: Seven studies were selected, all exploring predictive models to forecast ED daily visits for general care. MAPE and RMAE were used to measure models' accuracy. All models displayed good accuracy, with errors below 10%. CONCLUSIONS: Model selection and accuracy was found to be particularly sensitive to the ED dimension. While ARIMA-based and other linear models have good performance for short-time forecast, some machine learning methods proved to be more stable when forecasting multiple horizons. The inclusion of exogenous variables was found to be advantageous only in bigger EDs.

Application of AR and VR in hand rehabilitation: A systematic review.

BACKGROUND: The human hand is the part of the body most frequently injured in work related accidents, accounting for a third of all accidents at work and often involving surgery and long periods of rehabilitation. Several applications of Augmented Reality (AR) and Virtual Reality (VR) have been used to improve the rehabilitation process. However, there is no sound evidence about the effectiveness of such applications nor the main drivers of therapeutic success. OBJECTIVES: The objective of this study was to review the efficacy of AR and VR interventions for hand rehabilitation. METHODS: A systematic search of publications was conducted in October 2019 in IEEE Xplore, Web of Science, Cochrane library, and PubMed databases. Search terms were: (1) video game or videogame, (2) hand, (3) rehabilitation or therapy and (4) VR or AR. Articles were included if (1) were written in English, (2) were about VR or AR applications, (3) were for hand rehabilitation, (4) the intervention had tests on at least ten patients with injuries or diseases which affected hand function and (5) the intervention had baseline or intergroup comparisons (AR or VR intervention group versus conventional physical therapy group). PRISMA protocol guidelines were followed to filter and assess the articles. RESULTS: From the eight selected works, six showed improvements in the intervention group, and two no statistical differences between groups. We were able to identify motivators of patients' adherence, namely real-time feedback to the patients, challenge, and increased individualized difficulty. Automated tracking, easy integration in the home setting and the recording of accurate metrics may increase the scalability and facilitate healthcare professionals' assessments. CONCLUSIONS: This systematic review provided advantages and drivers for the success of AR/VR application for hand rehabilitation. The available evidence suggests that patients can benefit from the use of AR or VR interventions for hand rehabilitation.