Abstract

Introduction: Self-management encourages a person with a long-term condition (LTC) to solve problems, take decisions, locate and useresources and take actions to manage their condition.Aims and objectives: The aim of this paper is to discover appropriate knowledge to facilitate the self-management paradigm. For use ina computing platform, such knowledge must be expressed in digital form in a database.Methods: The SMART2 [1] project is developing a Personalised Self Management System (PSMS) for use in the home environment andin the immediate community for people living with the LTCs: stroke, chronic pain and congestive heart failure (CHF). This system relieson access to clinically validated digital media for therapeutic instruction and appropriate feedback, based on current use.Results: Two approaches to knowledge acquisition were used: (i) obtaining knowledge from the stakeholders, using a user-centred designapproach (ii) obtaining knowledge from the PSMS, as the user undertakes activities of daily living in pursuit of their end-goal. We haveutilized data mining and classification techniques to quantify PSMS interventions.Conclusions: Knowledge capture requires abstraction of key process used by the stakeholders and the use of data mining procedures toobtain information patterns, which can be used to promote self-management.