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An observational study on IAPT waiting times before, during and after the COVID-19 pandemic using descriptive time-series data

An observational study on IAPT waiting times before, during and after the COVID-19 pandemic using... The purpose of this paper is to explore waiting times in improving access to psychological therapies (IAPT) services before and throughout the COVID-19 pandemic. The paper aims to help develop a better understanding of waiting times in IAPT so that interventions can be developed to address them.Design/methodology/approachIAPT national data reports was analysed to determine access and in-treatment waiting times before, during and after the COVID-19 pandemic. Time-series data was used to examine referral patterns, waiting list size and waiting times between the period of November 2018 and January 2022. The data covers all regions in England where an IAPT service has been commissioned.FindingsThere was a dramatic drop in referrals to IAPT services when lockdown started. Waiting list size for all IAPT services in the country reduced, as did incomplete and completed waits. The reduction in waiting times was short-lived, and longer waits are returning.Practical implicationsThis paper aims to contribute to the literature on IAPT waiting times both in relation to, and outside of, COVID-19. It is hoped that the conclusions will generate discussion about addressing long waits to treatment for psychological therapy and encourage further research.Originality/valueTo the best of the authors’ knowledge, there is no published research examining the performance of IAPT waiting times to second appointment. The paper also contributes to an understanding of how IAPT waiting times are measured and explores challenges with the system itself. Finally, it offers an overview on the impact of the COVID-19 pandemic on waiting time performance nationally. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Mental Health Review Journal Emerald Publishing

An observational study on IAPT waiting times before, during and after the COVID-19 pandemic using descriptive time-series data

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References (66)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1361-9322
eISSN
1361-9322
DOI
10.1108/mhrj-04-2022-0023
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to explore waiting times in improving access to psychological therapies (IAPT) services before and throughout the COVID-19 pandemic. The paper aims to help develop a better understanding of waiting times in IAPT so that interventions can be developed to address them.Design/methodology/approachIAPT national data reports was analysed to determine access and in-treatment waiting times before, during and after the COVID-19 pandemic. Time-series data was used to examine referral patterns, waiting list size and waiting times between the period of November 2018 and January 2022. The data covers all regions in England where an IAPT service has been commissioned.FindingsThere was a dramatic drop in referrals to IAPT services when lockdown started. Waiting list size for all IAPT services in the country reduced, as did incomplete and completed waits. The reduction in waiting times was short-lived, and longer waits are returning.Practical implicationsThis paper aims to contribute to the literature on IAPT waiting times both in relation to, and outside of, COVID-19. It is hoped that the conclusions will generate discussion about addressing long waits to treatment for psychological therapy and encourage further research.Originality/valueTo the best of the authors’ knowledge, there is no published research examining the performance of IAPT waiting times to second appointment. The paper also contributes to an understanding of how IAPT waiting times are measured and explores challenges with the system itself. Finally, it offers an overview on the impact of the COVID-19 pandemic on waiting time performance nationally.

Journal

Mental Health Review JournalEmerald Publishing

Published: Nov 9, 2022

Keywords: Referrals; IAPT; Time-series data; Waiting lists; COVID-19; Waiting times

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