Towards personalized allocation of patients to therapists.

Abstract:

OBJECTIVE:Psychotherapy outcomes vary between therapists, but it is unclear how such information can be used for treatment planning or practice development. This proof-of-concept study aimed to develop a data-driven method to match patients to therapists. METHOD:We analyzed data from N = 4,849 patients who accessed cognitive-behavioral therapy in U.K. primary care services. The main outcome was posttreatment reliable and clinically significant improvement (RCSI) on the Patient Health Questionnaire-9 (PHQ-9) depression measure. Machine-learning analyses were applied in a training sample (N = 2,425 patients treated by 68 therapists in Year 1), including a chi-squared automatic interaction detector (CHAID) algorithm and a random forest (RF) algorithm. The predictive models were cross-validated in a statistically independent test sample (N = 2,424 patients treated by the same therapists in Year 2) and evaluated using odds ratios (ORs) adjusted for baseline depression severity. RESULTS:We identified subgroups of therapists that were differentially effective for highly specific subgroups of patients, yielding 17 classes of patient-to-therapist matches. The overall base rate of RCSI in the sample was 40.4%, but this varied from 10.5% to 69.9% across classes. Cases classed by the prediction algorithms as expected responders in the test sample were ∼60% more likely to attain posttreatment RCSI compared with those classed as nonresponders (adjusted ORs = 1.59, 1.60; p < .001). CONCLUSIONS:Machine-learning approaches could help to improve treatment outcomes by enabling the strategic allocation of patients to therapists and therapists to supervisors. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

journal_name

J Consult Clin Psychol

authors

Delgadillo J,Rubel J,Barkham M

doi

10.1037/ccp0000507

subject

Has Abstract

pub_date

2020-09-01 00:00:00

pages

799-808

issue

9

eissn

0022-006X

issn

1939-2117

pii

2020-31137-001

journal_volume

88

pub_type

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