Clark2018 - Transparency about the outcomes of mental health services (IAPT approach), and analysis of public data
- Type:#article
- Year read:#read2021
- Subject: IAPT
- Bibtex: @clark2018
- Bibliography: Clark, D. M., Canvin, L., Green, J., Layard, R., Pilling, S., & Janecka, M. (2018). Transparency about the outcomes of mental health services (IAPT approach): An analysis of public data. The Lancet, 391(10121), 679–686. https://doi.org/10.1016/S0140-6736(17)32133-5
Example citation
An analysis of publicly available data from the IAPT programme found that several organisation-level factors predicted the rate of reliable improvement and reliable recovery, and that changes in these factors could account for 22-33% of the variance in outcomes the following fiscale year [@clark2018].
More treatment sessions, fewer missed appointments and shorter waiting times for treatment predicted clinical outcomes in an analysis of public IAPT data [@clark2018].
Key takeaways
- Positive associations with outcomes: % cases with a problem descriptor (diagnosis), n treatment sessions, % referrals treated
- Negative associations: Time waited to start treatment, % appointments missed
- However, VERY small effect sizes (OR 1.002 to 1.077) that are significant due to the huge dataset.
- The organisational factors predicted 2014 -> 2015 changes in outcomes, R2 for reliable improvement = 0.33, R2 for reliable recovery = 0.22.
- Patient-level data was not available.
Predictors of variability in clinical performance. So service-level analysis.
Outcomes were reliable improvement and reliable recovery
