Streamlining the referral to treatment pathway in IAPT
Research type
Research Study
Full title
Streamlining the referral to treatment pathway using anonymised patient data from Improving Access to Psychological Therapies (IAPT) services to train and develop machine learning algorithms and generate insights relevant to service delivery and workflow improvement including therapist allocation, appointment scheduling, data recording and data quality, and eligibility for service.
IRAS ID
275965
Contact name
Alison Sturgess-Durden
Contact email
Sponsor organisation
Mayden House Ltd
Duration of Study in the UK
0 years, 3 months, 31 days
Research summary
This study will use anonymised patient data from Improving Access to Psychological Therapies (IAPT) services to train and develop machine learning algorithms and generate information relevant to service delivery and workflow improvement including therapist allocation, appointment scheduling, data recording and data quality, and eligibility for service during the referral-to-treatment care pathway.
Our hypothesis is that this will provide IAPT services with insights into patterns previously undiscovered in historical data, and therefore enable decisions between therapists and new patients to be based on richer information than has previously been available.
A 2017 Mental Health Foundation survey found that current levels of good mental health are low and that collective mental health is deteriorating. Like the rest of the NHS, those tasked with treating mental health are expected to do so under tight budgets and with limited resources. Every sub-optimal treatment course creates inefficiencies for those services. We aim to enable services to access useful/relevant information and patterns found in historical data which they may take into account when making decisions about the treatment pathway for new patients. The IAPT dataset is one of the most advanced datasets within the NHS, including routine collection of patient reported outcome measures. Once our models have been tested/verified/validated, we aim to provide information for Mental Health services that will streamline assessment pathways and have the potential to improve outcomes for patients. This study will differ from previous studies due to the size of the dataset, making use of years of data from a number of different services throughout the country.
The inclusion of these new analytical capabilities within web based applications for Mental Health services offers the prospect of making more informed decisions about the care pathway based on an in-depth analysis of historical data, thus improving the effectiveness of treatment.
REC name
N/A
REC reference
N/A