The work described in this paper extracts user rating information from collaborative filtering datasets, and for each dataset uses a supervised machine learning approach to identify if there is an underlying relationship between rating information in the dataset and the expected accuracy of recommendations returned by the system. The underlying relationship is represented by decision tree rules. The rules can be used to indicate the predictive accuracy of the system for users of the system. Thus a user can know in advance of recommendation the level of accuracy to expect from the collaborative filtering system and may have more (or less) confidence in the recommendations produced. The experiment outlined in this paper aims to test the accuracy of the rules produced using three different datasets. Results show good accuracy can be found for all three datasets.

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Handover of patient care is a time of particular risk and it is important that accurate and relevant information is clearly communicated. The hospital discharge letter is an important part of handover. However, the quality of hospital discharge letters is variable and letters frequently omit important information. The Cork Letter-Writing Assessment Scale (CLAS) checklist is an itemized checklist developed to improve the quality of discharge letters. The CLAS checklist, with an inbuilt scoring system, is available as the CLAS mobile application. Mobile applications offer an exciting opportunity for ‘point of practice' knowledge acquisition and are widely used by medical students. Content quality is integral to the success of educational mobile applications. In a recent study, the CLAS checklist improved the quality (content, structure and clarity) of discharge letters written by medical students. Though retention of these skills into the work-place and effects on patient safety have yet to be demonstrated, the development of standardized electronic discharge letters allows faster and safer transfer of information between healthcare providers and is a welcome advance. Using Near Field Communication for mobile applications to seamlessly transfer discharge letters between devices is another important feature.

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