Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data

The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year. The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.

The personality attributes and leisure activities of the internet users: a Taiwanese case study

This study aims to examine the relationship between the personality attributes of Internet users and their leisure activities. A questionnaire survey was undertaken which revealed that most Internet users are single males aged between 21-30 belonging to the lower income groups, employed in information technology or related fields. The personality attributes of the sample showed a tendency towards a mixed locus control category. The survey indicated that the preferred leisure activities of this population group are reading, collecting and computer-based activities. However, ‘movement’ and collecting were the only leisure activities to show a significant correlation with the users’ personality attributes.

Individual differences and personality correlates of navigational performance in the virtual route learning task

Research on the mechanisms and processes underlying navigation has traditionally been limited by the practical problems of setting up and controlling navigation in a real-world setting. Thanks to advances in technology, a growing number of researchers are making use of computer-based virtual environments to draw inferences about real-world navigation. However, little research has been done on factors affecting human–computer interactions in navigation tasks. In this study female students completed a virtual route learning task and filled out a battery of questionnaires, which determined levels of computer experience, wayfinding anxiety, neuroticism, extraversion, psychoticism and immersive tendencies as well as their preference for a route or survey strategy. Scores on personality traits and individual differences were then correlated with the time taken to complete the navigation task, the length of path travelled,the velocity of the virtual walk and the number of errors. Navigation performance was significantly influenced by wayfinding anxiety, psychoticism, involvement and overall immersive tendencies and was improved in those participants who adopted a survey strategy. In other words, navigation in virtual environments is effected not only by navigational strategy, but also an individual’s personality, and other factors such as their level of experience with computers. An understanding of these differences is crucial before performance in virtual environments can be generalised to real-world navigational performance.

Predictive Risk Modelling for Integrated Care: a Structured Review

If patients at risk of admission or readmission to hospital or other forms of care could be identified and offered suitable early interventions then their lives and long-term health may be improved by reducing the chances of future admission or readmission to care, and hopefully, their cost of care reduced. Considerable work has been carried out in this subject area especially in the USA and the UK. This has led for instance to the development of tools such as PARR, PARR-30, and the Combined Predictive Model for prediction of emergency readmission or admission to acute care. Here we perform a structured review the academic and grey literature on predictive risk tools for social care utilisation, as well as admission and readmission to general hospitals and psychiatric hospitals. This is the first phase of a project in partnership with Docobo Ltd and funded by Innovate UK,in which we seek to develop novel predictive risk tools and dashboards to assist commissioners in Clinical Commissioning Groups with the triangulation of the intelligence available from routinely collected data to optimise integrated care and better understand the complex needs of individuals.