Clinical trial NCT05495126: Evaluate Treatment Outcomes For AI-Enabled Information Collection Tool For Clinical Assessments In Mental Healthcare
Official title: Evaluate Treatment Outcomes For AI-Enabled Information Collection Tool For Clinical Assessments In Mental Healthcare
Study type: INTERVENTIONAL
Overall status: ACTIVE_NOT_RECRUITING
Phase: NA
Conditions: Mental Health Issue
Lead sponsor: Limbic Limited
Sponsor class: INDUSTRY
Collaborators: name: Everyturn Mental Health
First posted: 2022-08-10
Enrollment: 5400
Sex: ALL
Minimum age (years): 16
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

Detailed 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.

Eligibility criteria: 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)

Primary outcomes:
measure: Change from baseline depression score to after treatment
timeFrame: 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.
measure: Change from baseline anxiety score to after treatment
timeFrame: 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.
measure: Change in diagnosis
timeFrame: 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).
measure: Clinical assessment times
timeFrame: This measure will be available after the clinical assessment (up to average of 1 month from consenting).
measure: Waiting times for assessment
timeFrame: This measure will be available after the clinical assessment (up to average of 1 month from consenting).
measure: Waiting times for treatment
timeFrame: This measure will be available after the start of treatment (up to average of 4 month from consenting).
Secondary outcomes:
measure: Referral Dropout Rates
timeFrame: During chatbot interaction (day 1)
measure: Assessment Dropout Rates
timeFrame: At time point of treatment termination using standard IAPT definitions (assessed up to 3 months)
measure: Treatment Dropout Rates
timeFrame: At time point of treatment termination using standard IAPT definitions (assessed up to 3 months)
Other outcomes:
measure: Agreement rate between the probabilistic model prediction (in the Limbic Access +AI pathway) and the clinical diagnosis.
timeFrame: The diagnosis of the clinician will be assessed at time of the clinical assessment (assessed up to 1 month).
measure: Bias in the predictive power of the model with regards to particular patient demographics
timeFrame: Demographic data is captured at the point of referral on the day that participants gives their consent.
Locations:
city: Gosforth
country: United Kingdom
facility: Insight Healthcare
Study officials:
References: