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

NCT05495126 | Active Not Recruiting DMC

• 1 other identifier: Limbic-303303
• Study Type: interventional
• Target: 5,400
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Mental Health Issue

Outcome Measures

Primary Outcomes (6)

• Change from baseline depression score to after treatment

  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.

  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.

• Change from baseline anxiety score to after treatment

  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.

  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.

• Change in diagnosis

  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

  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).

• Clinical assessment times

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

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

• Waiting times for assessment

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

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

• Waiting times for treatment

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

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

Secondary Outcomes (3)

• Referral Dropout Rates

  During chatbot interaction (day 1)

• Assessment Dropout Rates

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

• Treatment Dropout Rates

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

Other Outcomes (2)

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

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

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

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

Study Arms (2)

Standard Limbic Access

ACTIVE COMPARATOR

In this arm, participants will refer through the standard pathway of Limbic Access. During this process patients provide the minimal required information (e.g. demographic information) as well as some basic information about their experienced mental health symptoms (e.g. PHQ-9 \& GAD-7). This information is attached to the referral provided to the clinician before the clinical assessment.

Diagnostic Test: Standard Limbic Access pathway

Limbic Access with AI

EXPERIMENTAL

In this arm, provide all information as in the standard Limbic Access pathway. Based on this information a machine-learning model is used to predict the most likely presenting problem, based on which up to two additional anxiety specific measures are administered in order to collect more tailored information about the patients' experienced mental health symptoms. All the information is attached to the referral provided to the clinician before the clinical assessment.

Diagnostic Test: Limbic Access with AI pathway

Interventions

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.

Standard Limbic Access

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.

Limbic Access with AI

Eligibility Criteria

• Age: 16 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Limbic Limited: lead
• Everyturn Mental Health: collaborator

Study Sites (1)

Insight Healthcare

Gosforth, NE13 9BA, United Kingdom

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 28, 2022
• First Posted: August 10, 2022
• Study Start: February 28, 2023
• Primary Completion: September 1, 2025
• Study Completion: December 1, 2025
• Last Updated: April 8, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)