Two-thirds of people in England, Scotland and Wales say that they have experienced a mental health problem, and collective mental health is deteriorating, according to The Mental Health Foundation (1). In 2008, the National Health Service (NHS) implemented the improving access to psychological therapies (IAPTs) programme across England, and this programme now sees over 1.6 million new referrals per year, with 193 services providing evidence-based treatments for people with depression and anxiety disorders, including children (2).
The first barrier to entering therapy is attending an appointment. Across England, 12% of IAPT appointments are missed, with this figure rising to 40% for first appointments. In order to deliver more efficient and effective care, it is important to encourage the patients who are most likely to miss their appointments to attend.
A KTP collaboration between the University of Bath led by Academic Supervisor, Dr Theresa Smith, Lecturer in the Department of Mathematical Sciences, and Mayden, who provide cloud based technologies to support patient care, aims to develop and test models to predict whether a patient will attend their therapy appointments over the course of their referral. The KTP Associate is Dr Alice Davis (pictured above). A recent output from this KTP is a paper published in BMJ’s Evidence Based Mental Health titled Predicting patient engagement in IAPT services: a statistical analysis of electronic health records which demonstrates the use of statistical modelling of IAPT data to predict first appointment attendance.
According to David Betts, Company Supervisor for the Innovate UK funded KTP project at Mayden “The KTP directly benefits Mayden’s strategic aims for expansion. Whilst our data services team is proficient at manipulating data, the KTP gives us the opportunities of specialist, academic, leading edge knowledge to expand our understanding of how predictive analytics, pattern recognition and computational statistics could be applied to complex patient datasets. Acquiring these capabilities and embedding them within the company allows the development of tools and commercial products for end users to make informed decisions and influence the behaviour of practitioners. The KTP is the ideal mechanism to keep us at the forefront of research in this area.“
The objectives of the research facilitated by the KTP are to “determine the feasibility of fitting a generalised linear mixed effects model (GLMM) to predict whether a patient will engage with their therapy, and whether a patient referred to an IAPT service will attend their first therapy appointment. This type of modelling approach has only been used for patient outcomes and has not been used previously to model attendance in IAPT. Attendance rates for first appointments are much lower than for subsequent appointments, and so that is the initial focus of the research.
This study utilises access to significantly more data than other research concerning appointment attendance and allows for a wider variety of patient characteristics available when a patient enters therapy, differing from existing research” according to the paper.
Geraint Jones, the Knowledge Transfer Adviser for the project commented “This is a perfect example of how KTP Associates leverage the expertise from the UK’s world class academic teams – providing improved economic performance for companies and wider social impact; invaluable data to benchmark and further the impact of research; and a fantastic career development opportunity for the Associate.”
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