Pediatric Population Management Classification for Children with Medical Complexity

Area of Science:

• Pediatric healthcare
• Health informatics
• Population health management

Background:

• Identifying children with medical complexity (CMC) for targeted care is challenging.
• Existing methods may not effectively capture sustained, high healthcare utilization.
• Proactive identification is crucial for quality improvement initiatives.

Purpose of the Study:

• To develop and validate a new system, the Pediatric Population Management Classification (PPMC), for identifying CMC.
• To compare the PPMC system with the Complex Chronic Conditions (CCC) system.
• To assess the ability of PPMC to capture longitudinal risk for elevated healthcare utilization.

Main Methods:

• Utilized real-time electronic health record (EHR) data from a tristate health system.
• Developed the PPMC based on visit diagnoses and problem lists.
• Compared PPMC with the CCC system using weighted kappa statistics for agreement and persistence of high charges/utilization (2016-2019).

Main Results:

• The PPMC system demonstrated higher precision in identifying CMC with persistently high utilization and charges, particularly within primary care settings.
• Agreement between PPMC and CCC was lower in primary care populations (weighted κ 62%) compared to specialty/hospital-only populations (weighted κ 82%).
• PPMC showed greater accuracy in capturing sustained high utilization among children receiving primary care.

Conclusions:

• The PPMC classification scheme offers a more pragmatic approach for selecting children with CMC for longitudinal care management.
• The PPMC system shows promise for improving proactive identification and management of high-risk pediatric populations.
• Real-time EHR data and refined classification systems are vital for effective population health management in pediatrics.