Predicting patient engagement in IAPT services: a statistical analysis of electronic health records

Alice Davis, Theresa Smith, Jenny Talbot, Chris Eldridge, David Betts

Research output: Contribution to journalArticle

Abstract

Abstract
Background Across England, 12% of all improving access to psychological therapy (IAPT) appointments are missed, and on average around 40% of first appointments are not attended, varying significantly around the country. In order to intervene effectively, it is important to target the patients who are most likely to miss their appointments.

Objective This research aims to develop and test a model to predict whether an IAPT patient will attend their first appointment.

Methods Data from 19 adult IAPT services were analysed in this research. A multiple logistic regression was used at an individual service level to identify which patient, appointment and referral characteristics are associated with attendance. These variables were then used in a generalised linear mixed effects model (GLMM). We allow random effects in the GLMM for variables where we observe high service to service heterogeneity in the estimated effects from service specific logistic regressions.

Findings We find that patients who self-refer are more likely to attend their appointments with an OR of 1.04. The older a patient is, the fewer the number of previous referrals and consenting to receiving a reminder short message service are also found to increase the likelihood of attendance with ORs of 1.02, 1.10, 1.04, respectively.

Conclusions Our model is expected to help IAPT services identify which patients are not likely to attend their appointments by highlighting key characteristics that affect attendance.

Clinical implications This analysis will help to identify methods IAPT services could use to increase their attendance rates.
Original languageEnglish
Pages (from-to)8-14
Number of pages7
JournalEvidence Based Mental Health
Volume23
Issue number1
Early online date11 Feb 2020
DOIs
Publication statusPublished - 11 Feb 2020

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