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
