Saunders, RJ; (2018) Development of a personalised approach to clinical decision making in psychological treatment services using routine patient data. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

This thesis is concerned with the development of a personalised treatment approach to aid clinical decisions in psychological interventions provided for common mental health disorders (CMHDs), such as depression and anxiety disorders. It begins with a discussion of personalised medicine in healthcare and its potential for optimising care for CMHDs. The thesis then considers how personalised medicine can be used to inform clinical decision making, specifically clinical decisions in relation to the delivery of treatment in mental health services. This includes a description of the types of clinical decisions required, as well as examples from across healthcare that have used decision support tools (DSTs) to aid clinical judgement. This is followed by a review of patient characteristics that have been associated with outcomes in CMHD treatment. The review is supplemented by an analysis of a large dataset (n=10693) of patients receiving psychological treatment for CMHDs. It explores the associations between routinely available patient characteristics and outcomes. The thesis then reports on the use of latent profile analysis using the patient characteristics to identify statistically distinct sub-groups (profiles) of patients, and considers the variation in treatment outcomes between profiles and by the intensity of treatment. The change in depression and anxiety symptoms, as measured at every treatment session, is statistically modelled to identify different trajectories of change within and between the latent profiles. These trajectories represent differential response to psychological treatment. Information from the identified profiles is combined with the within treatment change methods to develop a personalised treatment approach to decisions about appropriate treatment and also clinical decisions during the course of treatment. The thesis then presents a prototype algorithm that can identify profiles and the likely trajectories of change pre-treatment, before discussing the clinical implications of providing this algorithm in routine care, as well as future directions of research

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