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

Limbic Limited

Status: Active, not recruiting

Conditions: Mental Health Issue

Treatments: Diagnostic Test: Limbic Access with AI pathway; Diagnostic Test: Standard Limbic Access pathway

Study type: Interventional

Funder types: Industry; Other

Identifiers

NCT05495126

Limbic-303303

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 5,400 estimated patients

Sex: All

Ages: 16+ years old

Volunteers: No Healthy Volunteers

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)

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Double Blind

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5,400 participants in 2 patient groups

Standard Limbic Access

Active Comparator group

Description:

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.

Treatment:

Diagnostic Test: Standard Limbic Access pathway

Limbic Access with AI

Experimental group

Description:

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.

Treatment:

Diagnostic Test: Limbic Access with AI pathway