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NCT Number: NCT05495126

Evaluate Treatment Outcomes For AI-Enabled Information Collection Tool For Clinical Assessments In Mental Healthcare

In the proposed study, the investigators aim to test an AI-prototype which adaptively collects information about a patient's mental health symptoms at the time of referral in order to support and facilitate the clinical assessment.

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This study is active but is not currently recruiting participants.

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Key information

Conditions

Mental Health Issue

Age range

16 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Insight Healthcare

Gosforth, NE13 9BA, United Kingdom

About this study

In the proposed study, the investigators aim to test an AI-prototype which adaptively collects information about a patient's mental health symptoms at the time of referral in order to support and facilitate the clinical assessment.

The AI-system consists of a machine learning model which produces a probabilistic prediction about a patient's most likely presenting problems (ranking different diagnoses based on their probability) based on standard referral information collected through Limbic Access (e.g. free-text description of the patient's symptoms, GAD-7 & PHQ-9 etc). Based on the ML prediction, up to two additional anxiety disorder specific measures (ADSM) will be administered in order to collect additional insights about the specific mental health symptoms experienced by the patient (i.e. tailored to the specific patient). The collected ADSM scores will be attached to the final referral information in order to support and facilitate the clinical assessment and ultimately improve the diagnosis process while saving clinical time. For this trial, the AI-model will only function as a support tool for the clinical assessment by collecting additional data ahead of time.

Specifically, the investigators are interested in evaluating whether the AI supported information collection improves treatment outcomes, reliability of clinical assessment, reduces waiting and assessment times as well as reduces treatment drop out rates.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Participant meets minimum age requirements for the service
  • Participant's registered GP is within the IAPT CCG catchment area

Exclusion criteria

  • Participants who are in crisis (defined by requiring urgent care or being at an urgent risk of harm)

Treatment and study plan

Standard Limbic Access pathway

Diagnostic Test

Relevant information for clinical referral (e.g. demographics) and basic clinical information (e.g. PHQ-9 & Gad-7 scores) are collected during the self-referral process which is then attached to the referral notes in order to facilitate the clinical assessment conducted by the clinician.

Limbic Access with AI pathway

Diagnostic Test

The same information as in the Limbic Access pathway is collected. However, additional information (i.e. disorder specific questionnaires) are collected for the most likely problem descriptors based on the ML-model predictions.

All information is attached to the referral in order to facilitate the clinical assessment conducted by the clinician.

Primary outcomes

  1. Change from baseline depression score to after treatment

    Time frame: The definition of reliable and clinically significant improvement is based on a comparison of pre-treatment (at time of referral, on the day of consenting) and post-treatment (assessed at point of discharge, an average of 5 months) clinical score.

    The primary outcome will be defined as reliable and clinically significant improvement in clinical scores after treatment. Hereby, the investigators will test for changes in depression scores using Patient Health Questionnaire-9 (PHQ-9: posttreatment scores <10 and improved by ≥6 points). PHQ-9 includes 9 questions scored between 0 and 3, with higher scores indicating more severe depression.

  2. Change from baseline anxiety score to after treatment

    Time frame: The definition of reliable and clinically significant improvement is based on a comparison of pre-treatment (at time of referral, on the day of consenting) and post-treatment (assessed at point of discharge, an average of 5 months) clinical score.

    The primary outcome will be defined as reliable and clinically significant improvement in clinical scores after treatment. Hereby, we will test for changes in anxiety scores using Generalised Anxiety Disorder Assessment (GAD-7: posttreatment scores <8 and improved by ≥4 points).GAD-7 includes 7 questions scored between 0 and 3, with higher scores indicating more severe anxiety.

  3. Change in diagnosis

    Time frame: The agreement score will be based on a comparison of diagnosis at the initial assessment (before first treatment session) and the diagnoses at the end of treatment (assessed at point of discharge, an average of 5 months from referral).

    Improved diagnosis will be measured as the correspondence between the diagnosis at the initial clinic assessment and the diagnosis at the end of treatment. During treatment in IAPT the diagnoses will be continuously assessed during the course of treatment in order to step the treatment up or down if needed. The agreement of diagnoses at these two time points will be coded as a binary variable ("agreement" versus "disagreement").

    The investigators will measure the percentage of patients for which the diagnosis at clinical assessment corresponds to the diagnoses at the end of treatment as a measure for the reliability for the initial diagnosis

  4. Clinical assessment times

    Time frame: This measure will be available after the clinical assessment (up to average of 1 month from consenting).

    Improved clinical efficiency will be indicated by reduced assessment times, measured by the average time per clinical assessment (in minutes).

  5. Waiting times for assessment

    Time frame: This measure will be available after the clinical assessment (up to average of 1 month from consenting).

    Patient waiting times for assessment will be measured as the time between the date of self-referral and the date of the clinical assessment.

  6. Waiting times for treatment

    Time frame: This measure will be available after the start of treatment (up to average of 4 month from consenting).

    Patient waiting times for treatment will be measured as the time between the date of assessment and the date of the first treatment session

Secondary outcomes

  1. Referral Dropout Rates

    Time frame: During chatbot interaction (day 1)

    Patient referral dropout will be measured as any individual who consented to participate in the study, but did not complete all requested clinical information during the referral process.

  2. Assessment Dropout Rates

    Time frame: At time point of treatment termination using standard IAPT definitions (assessed up to 3 months)

    Clinical assessment dropout will be measured as any cancellation or "Did Not Attend" event for patients who successfully had a clinical assessment slot (eg. time and date) organised. The treatment cohort (Limbic Access with AI pathway) will be evaluated against a cohort of patients going through limbic Access' standard pathway across the same services and over the same time window as the study will be used for comparison.

  3. Treatment Dropout Rates

    Time frame: At time point of treatment termination using standard IAPT definitions (assessed up to 3 months)

    Treatment dropout will be measured using a "dropout" label which is added to a patient's file in the service's patient management system by the treating clinician when a dropout event occurs. The treatment cohort (Limbic Access +AI pathway) will be evaluated against a cohort of patients going through limbic Access' standard pathway across the same services and over the same time window as the study will be used for comparison.

Other outcomes

  1. Agreement rate between the probabilistic model prediction (in the Limbic Access +AI pathway) and the clinical diagnosis.

    Time frame: The diagnosis of the clinician will be assessed at time of the clinical assessment (assessed up to 1 month).

    Kappa for each diagnosis will be calculated as agreement score between the model prediction and the diagnosis at clinical assessment.

  2. Bias in the predictive power of the model with regards to particular patient demographics

    Time frame: Demographic data is captured at the point of referral on the day that participants gives their consent.

    Percentage of agreement between model prediction and clinical diagnosis for different demographic groups

Sponsors and collaborators

Lead sponsor

Limbic Limited

Industry

Collaborators

  • Everyturn Mental Health

Registry information

Important dates

Study start
2023
Primary completion
2025
Study completion
2025
First posted
Aug 10, 2022
Registry last updated
Apr 8, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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