Evaluate Treatment Outcomes For AI-Enabled Information Collection Tool For Clinical Assessments In Mental Healthcare
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.
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.
Inclusion Criteria:
Exclusion Criteria: