Evaluate Treatment Outcomes For AI-Enabled Information Collection Tool For Clinical Assessments In Mental Healthcare
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 5,400
- 试验地点
- 2
- 主要终点
- Waiting times for assessment
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Double (Participant, Outcomes Assessor)
入排标准
- 年龄范围
- 16 Years 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Participant meets minimum age requirements for the service
- •Participant's registered GP is within the IAPT CCG catchment area
排除标准
- •Participants who are in crisis (defined by requiring urgent care or being at an urgent risk of harm)
结局指标
主要结局
Waiting times for assessment
时间窗: 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.
Clinical assessment times
时间窗: 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).
Change from baseline anxiety score to after treatment
时间窗: 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.
Change in diagnosis
时间窗: 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
Waiting times for treatment
时间窗: 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
Change from baseline depression score to after treatment
时间窗: 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.
次要结局
- 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))