Improving the efficiency of psychological treatment using outcome feedback technology
Introduction
Several studies have demonstrated that monitoring patients' response to psychological treatment using standardised outcome measures can help to detect difficulties and to improve outcomes for patients (Gondek, Edbrooke-Childs, Fink, Deighton, & Wolpert, 2016). Routine outcome monitoring may be particularly important for certain patients that tend to have a poorer response to treatment (Lambert et al., 2001, Lutz et al., 2015), referred to as ‘signal cases’ or cases that are ‘not on track’ (NOT). Lambert et al. (2003) proposed that providing timely feedback to therapists using psychometric measures to alert them about signal cases could help to improve their outcomes. Typically, outcome feedback (OF) involves entering a patient's symptom measures into a computer system that graphically displays changes from session-to-session, comparing these to clinical norms derived from hundreds of similar cases. Patients with symptoms that do not improve as suggested by these clinical norms are flagged up as NOT. A meta-analysis of controlled trials in USA concluded that NOT cases in usual psychological care were 2.3 times more likely to deteriorate by comparison to NOT cases treated by therapists that apply OF technology (Shimokawa, Lambert, & Smart, 2010). However, this meta-analysis included studies from the same research group which predominantly treated student populations, therefore raising some questions about generalizability (Davidson, Perry, & Bell, 2015). More recently, trials in European countries have replicated these findings in other clinical populations, suggesting that using OF can help to prevent deterioration in NOT cases (e.g., Amble et al., 2015, De Jong et al., 2014, Hansson et al., 2013).
Although the usefulness of outcome feedback has been demonstrated in specialist counselling and psychotherapy centres, these methods have not yet been tested in stepped care psychological services such as those linked to the IAPT (Improving Access to Psychological Therapies) model applied in England (Clark, 2011) and Australia (Cromarty, Drummond, Francis, Watson, & Battersby, 2016). IAPT services are particularly well placed to apply OF methods since they routinely collect standardised outcome measures at every session to monitor clinical outcomes (Clark, 2011). However, the high volume of work and time pressures typical of public healthcare settings may limit therapists' ability to consistently and meaningfully reflect on the results of outcome measures within their treatment sessions. Furthermore, research suggests that IAPT clinicians do not necessarily consider symptom measures in their decisions about treatment planning and some tend to rely on subjective beliefs and attitudes when making decisions about the treatment of non-improving patients (Delgadillo, Gellatly, & Stephenson-Bellwood, 2015). Therefore, there are plausible contextual and attitudinal barriers that may limit the effective utilization of outcome feedback in this setting.
This study presents the first application of outcome feedback technology in an IAPT stepped care context. The primary objective of the study was to evaluate the clinical impact of using OF, quantified in terms of changes in symptoms, treatment duration and cost. A secondary objective was to assess the feasibility and acceptability of discussing OF with patients in weekly therapy sessions.
Section snippets
Setting, interventions and study design
This study was conducted in an IAPT stepped care service in Leeds, a large and socioeconomically diverse city in the north of England. The service offered evidence-based and protocol-driven psychological interventions for depression and anxiety problems, guided by routine session-by-session outcomes monitoring, consistent with clinical guidelines (National Institute for Health and Care Excellence, 2011). According to publicly available data for the period of the study (NHS Digital, 2016), 6410
Quantitative data on clinical impact
Fixed effects of the fully adjusted MLM analysis are shown in Table 2. The cohort * time interaction term represents the main between-group comparison in symptom changes across time. This was not statistically significant in PHQ-9 (B = 0.80, SE = 0.78, p = 0.30) or GAD-7 (B = 0.80, SE = 0.71, p = 0.26) models applied in the full sample, nor in the subsamples of NOT (shown in Table 2) or OT cases.
Case-mix adjusted logistic regressions (Table 3) indicated that RCSI rates were not significantly
Main findings
This study presents the first comprehensive evaluation of outcome feedback technology applied in a stepped care psychological treatment setting. The results indicated that this technology was feasible to adopt in routine care, was minimally burdensome, and was generally seen by therapists and patients as a useful aid to decision-making and clinical supervision processes.
Qualitative interviews with therapists revealed that the outcome feedback signalling technology influenced their
Conflict of interest
None.
Acknowledgements
The Leeds Outcome Feedback Study (NHS REC Reference: 15/NW/0675) was supported by research capability funding awarded by Leeds Community Healthcare NHS Trust. The outcome feedback and signalling technology used in this study was developed by PCMIS at the Department of Health Sciences, University of York (http://www.pc-mis.co.uk). We thank Byron George, Gareth Percival, Colin Robson, Alexander Teahan, Jan Thomson, Simon Day, Anne Briggs, Jan Lewis, Abigail Coe, Sarah West, Caroline Lloyd, Angela
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