Pediatric Asthma Surveillance System: Artificial Intelligence/Machine Language-Driven Chronic Disease Surveillance Blueprint

Event Time

Originally Aired - Thursday, March 14 1:00 PM - 2:00 PM Eastern Time (US & Canada)

Info Alert

Create or Log in to My Show Planner to see Videos and Resources.

Info Alert

Create or Login to MyHIMSSPlanner to build your schedule and see more information.

Videos

Resources

Create or Log in to My Show Planner to see Videos and Resources.


{{chatHeaderContent}}

{{chatBodyContent}}

Resources

Create or Log in to My Show Planner to see Videos and Resources.


Info Alert

This Session Has Not Started Yet

Be sure to come back after the session starts to have access to session resources.

Event Location

Location: W209C


Event Information

Type: General Education

Session ID: 213

Title: Pediatric Asthma Surveillance System: Artificial Intelligence/Machine Language-Driven Chronic Disease Surveillance Blueprint

Description: The presenters built an AI- ML-driven pediatric asthma surveillance system (PASS) to monitor the clinical and social risk of pediatric asthma at the census tract level in Dallas County. First, they developed a novel AI/ML pediatric asthma risk index, combining clinical and social risk factors from multiple data sources to accurately predict census-tract risk of asthma-related emergency department visits and hospitalizations. Subsequent analyses identified actionable risk drivers which, combined with the novel asthma risk index, painted a wholesome, countywide picture of pediatric asthma risk disparities. PASS is an interactive, community-facing dashboard that maps and compares the distribution of the asthma risk index and other risk drivers across Dallas County. PASS is hosted on the Dallas County Health and Human Services website and is readily accessible to community stakeholders. Launched in January 2023, PASS was introduced to the community through training sessions and dissemination events to engage key stakeholders. PASS is being leveraged to advance health equity through diverse use cases ranging from environmental advocacy to city planning, clinical resources deployment, school-based interventions and corporate social responsibility. Lessons learned from PASS provide a blueprint for other scalable AI/ML-driven chronic disease surveillance systems such as diabetes and hypertension.

Level: Advanced

Format: 60-Minute Lecture

Learning Objective #1: Describe the benefits of building a community-wide pediatric asthma risk surveillance system and identify key community stakeholders to engage for its successful implementation

Learning Objective #2: Identify key data sources necessary to develop an AI/ML-driven community-level pediatric asthma risk index that combines clinical and social risk factors to predict the risk for asthma-related ED visits or hospitalizations at a microgeographical level amenable to interventions (e.g., census tract, block group)

Learning Objective #3: Identify the challenges of and mitigating approaches to building and deploying an AI/ML-driven community-level pediatric asthma risk index and surveillance system

Learning Objective #4: List three key insights about asthma risk disparities and three actionable asthma risk drivers identified using PASS in Dallas County

Learning Objective #5: Describe five ways in which community stakeholders can use AI/ML-driven chronic disease surveillance systems to advance community health equity


Speakers


Continuing Education Credits

  • ACPE – 1 Credit(s)
  • CAHIMS – 1 Credit(s)
  • CHES/MCHES – 1 Credit(s)
  • CME – 1 Credit(s)
  • CNE – 1 Credit(s)
  • CPD UK – 1 Credit(s)
  • CPHIMS – 1 Credit(s)
  • PMI/PDU – 1 Credit(s)

  • Tracks


    Categories

    Health Equity

    • Health Disparities and Inequities

    Audience

    • Data Scientist
    • Government or Public Policy Professional
    • Public Health Practitioner