Electronic Health Record-Based Clinical Predictive Modeling to Match Care Coordination to Children with Complex Health Needs

November 16, 2022
12:00 pm to 1:00 pm

Event sponsored by:

AI Health
+DataScience (+DS)
Biostatistics and Bioinformatics
Center for Computational Thinking
Computer Science
Electrical and Computer Engineering (ECE)
Pratt School of Engineering


Duke AI Health



David Ming, MD, Internist and Pediatrician, Hospital Medicine Specialist and
Richard Chung, MD Pediatrician, Adolescent Medicine Specialist with host
Andrew Olson, MPP, Associate Director, Policy Strategy and Solutions for Health Data Science, Duke AI Health

Identification of high-risk children with complex health needs (CCHN) who may benefit most from care coordination to support intersecting medical and social needs is challenging yet essential to meet the health needs of children with high needs and their families. In this seminar, we will discuss the findings and lessons learned from development and implementation of the Pediatric Complex Care Integration (PCCI) program, in which we used machine learning clinical decision support and electronic health record (EHR) data-based predictive modeling to match complex care coordination services to CCHN within primary care at Duke.

We will highlight how lessons learned from this program advanced understanding of:
(a) how to implement child-specific clinical predictive modeling in real-world environments; and
(b) how to implement and evaluate the risk prediction data model in clinical practice using pragmatic methods.

We will also discuss opportunities to build on this project's blueprint and disseminate data-driven programs for use at the multi-site and population levels in alignment with value-based priorities and performance targets of health systems and payers.