A framework for evaluating the European airline costs of disabled persons and persons with reduced mobility

In recent years, airlines have been servicing a greater variety, and increasing numbers, of disabled persons and persons with reduced mobility (PRMs), particularly associated with ageing, obesity and medical needs. With the quantity of PRMs likely to increase in the future, there will be a growing impact on the airlines' associated actual and opportunity costs, about which there is minimal literature and data. Therefore the aim of this paper is to identify standard functional key factors (FKFs) with which airlines could audit their PRMs costs, and which could be used by other interested bodies, such as governments, when considering relevant aviation policy. These FKFs are related to nine areas, namely PRMs’ transfers; mobility aids; aircraft delays/diversions costs; staff training costs; staff health, safety and welfare; aircraft fixtures and equipment costs; airport costs; transaction costs; and opportunity costs. Further research is needed to obtain the data for these FKFs.

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.