RS2 - Unraveling Complexity: Applying Machine Learning to Identify and Support Youth at Risk for Co-occurring Disorders

Tuesday, April 16, 2024

1:00 PM – 2:00 PM CT

Location: 100/101, Level 1, ACCC

Workshop Presenter(s)

KS

Kristopher I. Stevens, PhD

Director of Data Analytics and Innovation
Pacific Clinics
Arcadia, California

Slides
JP

Jorge R. Petit, MD

President and CEO
Coordinated Behavioral Care
New York, New York

Presentation Description: Our presentation leverages AI/ML to identify COD risks in over 4,300 youth aged 12-24, tailoring preventive interventions. Participants grasp AI/ML's precision in COD identification, gaining actionable insights for youth mental health strategies. We explore an AI-driven web app's potential for tailored treatments, enhancing outcomes. We discuss AI/ML's role in aligning data with impactful behavioral health care strategies. Participants embark on a transformative journey, discovering technology-driven, empathy-focused interventions' role in shaping youth behavioral health. Our presentation highlights AI/ML's potential in addressing COD in youth behavioral health, emphasizing the synergy of technology and empathy for impactful care.

Learning Objectives:

Explore the associations between complex mental health needs and substance use risks among youth receiving mental health services in a community based setting.
Illustrate the transformative potential of AI/ML techniques applied to EHRS data to identify youth at-risk for developing co-occurring substance use disorders and potentially more severe psychopathology.
Build the capacity for actionable data analytics within your organization to enhance the effectiveness of services by identifying youth at increased risk for developing co-occurring substance use disorders.