Management of Autism Spectrum Disorder in Italian Units of Child and Adolescent Mental Health: Diagnostic and Referral Pathways.
Marta BorgiFlavia ChiarottiGianfranco AresuFilippo GittiElisa FazziAngiolo PieriniTeresa SebastianiMarco MarcelliRenato ScifoPaolo StagiAldina VenerosiPublished in: Brain sciences (2022)
Overall, the present pilot study provides detailed information on clinical management for Autism Spectrum Disorder (ASD) referral and diagnosis processes that are mandatory for child and adolescent mental health management. The analysis of ASD management, even if carried out on a selected sample of Child and Adolescent Mental Health (CAMH) units, represents a good approximation of how, in Italian outpatient settings, children and adolescents with ASD are recognised and eventually diagnosed. One of the aims of the study was to verify the adherence of Italian CAMH units to international recommendations for ASD referral and diagnosis and whether these processes can be traced using individual chart reports. Overall, the analysis evidenced that Italian CAMH units adopt an acceptable standard for ASD diagnosis, although the reporting of the ASD managing process in the individual chart is not always accurate. Furthermore, data collected suggest some improvements that CAMH units should implement to fill the gap with international recommendations, namely, establishing a multidisciplinary team for diagnosis, improving the assessment of physical and mental conditions by the use of standardised tools, implementing a specific assessment for challenging behaviours that could allow timely and specific planning of intervention.
Keyphrases
- mental health
- autism spectrum disorder
- attention deficit hyperactivity disorder
- intellectual disability
- mental illness
- primary care
- randomized controlled trial
- emergency department
- healthcare
- clinical practice
- physical activity
- big data
- adipose tissue
- working memory
- insulin resistance
- artificial intelligence
- health information
- data analysis
- clinical evaluation
- drug induced
- deep learning