Crossing heterogeneous information sources for better analysis of health and social care data

In this paper we describe a methodology that emerged during an implementation of a health-and-social-care-oriented data repository, which consists in grouping information from heterogeneous and distributed information sources. We developed this methodology by first constructing a concrete data repository, containing information about elderly patients flows in the UK's long-term care (LTC) system. In our specific case, the role of the data repository is to allow knowledge extraction about consumption and behavioural tendencies in the elderly people population within the LTC system. These tendencies can be depicted in terms of survival behaviour (modelling), cost evolution, and bed use. Other types of knowledge that can be extracted are typical patient profiles, placement policy in term of rules and criteria effectively applied and, specific features of the business process behind the long-term care provision. A well-constructed data repository can support the discovery (analysis) of hidden aspects about the way patients are placed and accepted in the LTC, and also how the allocated resources are consumed. We argue that the use of this methodology could save time in similar undertakings or in other fields than health and social care.