Mood instability and mental health service use in autism and attention-deficit/hyperactivity disorder: a natural language processing analysis of CRIS electronic healthcare records from 21 906 children and adolescents - Scorecard - MDSpire
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Mood instability and mental health service use in autism and attention-deficit/hyperactivity disorder: a natural language processing analysis of CRIS electronic healthcare records from 21 906 children and adolescents
Clinical Scorecard: Exploring the Relationship Between Mood Instability and Utilization of Mental Health Services in Children with Autism and ADHD: A Natural Language Processing Study of CRIS Electronic Health Records from 21,906 Youth
At a Glance
Category
Detail
Condition
Mood Instability in Children with Autism and ADHD
Key Mechanisms
Association with increased annual CAMHS use and reduced time to discharge in ADHD group.
Target Population
Children and young people with neurodevelopmental diagnoses (ASD and/or ADHD).
Care Setting
Child and adolescent mental health services (CAMHS)
Key Highlights
Mood instability linked to higher annual CAMHS use in ASD and ADHD groups.
In ADHD group, mood instability associated with shorter time to discharge.
Effects of mood instability most pronounced in ADHD-only group not receiving medication.
Natural language processing (NLP) effectively identifies mood instability in clinical records.
Emotion dysregulation is a significant factor influencing CAMHS utilization.
Guideline-Based Recommendations
Diagnosis
Assess emotion dysregulation, including mood instability, at diagnosis.
Management
Consider care pathway allocation based on baseline mood instability, especially in ADHD.
Monitoring & Follow-up
Monitor CAMHS utilization patterns in relation to mood instability.
Risks
Increased risk of co-occurring mental health conditions linked to mood instability.
Patient & Prescribing Data
Children and young people with ASD and/or ADHD referred to CAMHS.
Mood instability may benefit from pharmacotherapy in ADHD.
Clinical Best Practices
Utilize NLP methods for scalable analysis of clinical data.
Incorporate findings on mood instability into care planning.
