Cost-effectiveness of feedback-informed psychological treatment: Evidence from the IAPT-FIT trial
Bibliographic record
Abstract
Background: Feedback-informed treatment (FIT) involves using computerized routine outcome monitoring technology to alert therapists to cases that are not responding well to psychotherapy, prompting them to identify and resolve obstacles to improvement.In this study, we present the first health economic evaluation of FIT, compared to usual care, to enable decision makers to judge whether this approach represents a good investment for health systems.Methods: This randomised controlled trial included 2233 patients clustered within 77 therapists who were randomly assigned to a FIT group (n = 1176) or a usual care control group (n = 1057).Treatment response was monitored using patient-reported depression (PHQ-9) and anxiety (GAD-7) measures.Therapists in the FIT group had access to a computerized algorithm that alerted them to cases that were "not on track", compared to normative clinical data.Health service costs included the cost of training therapists to use FIT and the cost of therapy sessions in each arm.The incremental cost-effectiveness of FIT was assessed relative to usual care, using multilevel modelling.Results: FIT was associated with an increased probability of reliable symptomatic improvement by 8.09 percentage points (95% CI: 4.16%-12.03%)which was statistically significant.The incremental cost of FIT was £15.17 (95% CI: £6.95 to £37.29) per patient and was not statistically significant.The incremental costeffectiveness ratio (ICER) per additional case of reliable improvement was £187.4 (95% CI: £126.7 to £501.5); this confidence interval shows that the relative cost-effectiveness is between FIT being a dominant strategy (i.e. more effective and also cost-saving) to FIT being more effective at a modest incremental cost to the health system.Conclusions: The FIT strategy increases the probability of reliable improvement in routine clinical practice and may be associated with a small (but uncertain) incremental cost.FIT is likely to be a cost-effective strategy for mental health services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".