Advancing personalised care: treatment selection and outcome prediction research

Personalized medicine, defined as “the use of marker-assisted diagnosis and targeted therapies” (Ginsburg & McCarthy, 2001), has enhanced health and preventive care for conditions such as cardiovascular problems, cancer and osteoporosis. By comparison, psychological services lag behind in the development and application of evidence-based personalization methods. Some notable advances in this regard are the development of patient profiling, treatment-selection and outcome prediction methods in psychological care. These methods use patient-data (e.g., demographics, psychometric tests and clinical assessment information) to predict which patients are more likely to benefit from specific treatment options, to assess how patients are responding to treatment and to detect risks early enough to prevent poor outcomes. Emerging studies in this field have shown that it is possible to improve the outcomes of therapy when therapists (and patients) receive ‘feedback’ using outcome monitoring and prediction systems. Furthermore, it appears that patients with certain characteristics tend to respond more favourably to certain treatments, for example CBT versus IPT for depression, or low versus high intensity CBT for common mental health problems.

The aims of this research programme are to develop treatment selection and outcome prediction methods for use in routine psychological care. Two strands of work may be pursued by PhD / DClinPsy students interested in this area: (1) the development and validation of prediction methods; (2) assessing the feasibility and acceptability of applying these methods in routine care.