Integrating WiFi based positioning with a job management system to study task management behaviour

Focus groups, interviews and anecdotal evidence suggest that senior clinicians are better than their juniors at managing their task load when working `out of hours´ in hospitals. For example experience allows them to prioritise cases which are likely to degrade and to organise their time to account for personal needs such as rest and refreshment. Quantifying this behaviour, and the variations between staff groups, is a complex task. Traditional direct observation and self-report methods are very intrusive, expensive and lack both scalability and validity. In this work we propose the use of positioning technology to augment or replace these traditional methods. We integrate contextual information from a digital task management system with location information to obtain a temporally ordered list of completed tasks with associated timings. The positioning system described in this work is based upon observations of visible WiFi access points. As the clinician moves between wards the set of visible access points changes and can be used to infer location. We propose a method by which access points can be associated with a discrete set of locations. This method removes the need for an expensive, intrusive `ground survey´ and is mindful of user privacy by only providing location within the pre-defined set. This paper describes the structure of the problem and a method for the integration of contextual and WiFi visibility data. Exemplar results are given from a limited scale trial performed in a large UK teaching hospital. The novel application of positioning technology to the study of clinical workplace behaviour offers opportunities to drive efficiencies, enhance staff training and hence improve patient safety.