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Using conversational AI to facilitate mental health assessment and improve clinical efficiencies in psychotherapy services in large real-world dataset
ABSTRACT
Background:
Most mental health service providers face the challenge of increasing demand in the absence of increases in funding or staffing. To overcome this supply-demand imbalance, providers need to increase efficiencies to cope with the demand.
Objective:
Here, we test whether artificial intelligence (AI) enabled solutions can enable mental health practitioners to use their time more efficiently, and thus reduce strain on the service and improve patient outcomes.
Methods:
In this study, we focus on the usage of an AI solution (Limbic Access) in the referral and assessment process in UK’s national health service (NHS) first-line psychotherapy service. Data was collected from 9 Improving Access to Psychological Therapies (IAPT) services across England from 64,862 patients.
Results:
We show that the use of this AI solution improves clinical efficiency by reducing the time clinicians spend on mental health assessments. Furthermore, we find improved outcomes for patients using the AI solution in a number of key metrics, such as reduced wait times, re- duced dropout rates, improved allocation to accurate treatment pathways and, most importantly, improved recovery rates. When investigating the mechanism by which the AI solution achieved these improvements, we find that the provision of clinically relevant information ahead of a clinical assessment was critical for these observed effects.
Conclusions:
Our results emphasise the utility of using AI solutions to support the mental health workforce and highlight that AI solutions can increase efficiencies and in parallel improve mental healthcare for patients.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.