Evidence map›Paper›PMID 38875569›Full record

ArticleJMIR AI2023

Using Conversational AI to Facilitate Mental Health Assessments and Improve Clinical Efficiency Within Psychotherapy Services: Real-World Observational Study.

Max Rollwage, Johanna Habicht, Keno Juechems, Ben Carrington, Sruthi Viswanathan, Mona Stylianou, Tobias U Hauser, Ross Harper

Abstract read
In one paragraph

Article in JMIR AI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Max Rollwage *Limbic Limited, London, United Kingdom.ORCID https://orcid.org/0000-0003-4181-3983
Johanna HabichtLimbic Limited, London, United Kingdom.ORCID https://orcid.org/0000-0001-5043-7129
Keno JuechemsLimbic Limited, London, United Kingdom.ORCID https://orcid.org/0000-0002-7887-175X
Ben CarringtonLimbic Limited, London, United Kingdom.ORCID https://orcid.org/0009-0005-1990-0724
Sruthi ViswanathanLimbic Limited, London, United Kingdom.ORCID https://orcid.org/0000-0002-1113-7171
Mona StylianouEveryturn Mental Health, Gosforth, United Kingdom.ORCID https://orcid.org/0009-0008-2268-5019
Tobias U HauserLimbic Limited, London, United Kingdom.ORCID https://orcid.org/0000-0002-7997-8137
Ross HarperLimbic Limited, London, United Kingdom.ORCID https://orcid.org/0000-0002-2403-2088

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMost mental health care providers face the challenge of increased demand for psychotherapy in the absence of increased funding or staffing. To overcome this supply-demand imbalance, care providers must increase the efficiency of service delivery.

objectiveIn this study, we examined whether artificial intelligence (AI)-enabled digital solutions can help mental health care practitioners to use their time more efficiently, and thus reduce strain on services and improve patient outcomes.

methodsIn this study, we focused on the use of an AI solution (Limbic Access) to support initial patient referral and clinical assessment within the UK's National Health Service. Data were collected from 9 Talking Therapies services across England, comprising 64,862 patients.

resultsWe showed that the use of this AI solution improves clinical efficiency by reducing the time clinicians spend on mental health assessments. Furthermore, we found improved outcomes for patients using the AI solution in several key metrics, such as reduced wait times, reduced dropout rates, improved allocation to appropriate treatment pathways, and, most importantly, improved recovery rates. When investigating the mechanism by which the AI solution achieved these improvements, we found that the provision of clinically relevant information ahead of clinical assessment was critical for these observed effects.

conclusionsOur results emphasize the utility of using AI solutions to support the mental health workforce, further highlighting the potential of AI solutions to increase the efficiency of care delivery and improve clinical outcomes for patients.

Indexed as

artificial intelligenceassessmentchatbotconversational agentdecision-supportIAPTImproving Access to Psychological Therapiesmental healthmental health assessmentNational Health ServiceNHSpsychotherapyreferralTalking Therapiestriage

Identifiers

PMID38875569
PMCPMC11041479

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.