139 - Pediatric Population Management Classification: Population Health Classification System for Children with Medical Complexity

Poster Number: 139
Publication Number: 139.304

Christian Pulcini, University of Vermont, Shelburne, VT, United States; Xianqun Luan, Childrens Hospital of Philadelphia, Philadelphia, PA, United States; Elizabeth Brooks, Childrens Hospital of Philadelphia, Philadelphia, PA, United States; Tina Penrose, Children's Hospital of Philadelphia, Philadelphia, PA, United States; Chen C. Kenyon, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States; Annique Hogan, Childrens Hospital of Philadelphia, Philadelphia, PA, United States; David Rubin, Children's Hospital of Philadelphia, Philadelphia, PA, United States

Background: Significant challenges persist in improving the overall care of children with medical complexity (CMC). These challenges are often related to difficulty in identifying the population for clinical care, quality improvement, and research initiatives. As health systems seek to operationalize registry definitions of CMC for these activities, an important goal is to ensure that the children they identify are at greater risk of sustained and elevated utilization, which is not a feature of current classification systems.

Objective: To create a Pediatric Population Management classification (PPMC) system to better capture longitudinal risk for sustained and elevated utilization across time to improve care in this vulnerable population.

Design/Methods: We reviewed the Electronic Health Record of a health system that accounts for approximately 500,000 individual child visits annually, with 29,313 inpatient admissions and 1,379,199 ambulatory visits across a tri-state region in 2019. All problem list diagnoses from the active problem list and visits to the health system at any location within the prior year were reviewed. Four years of data in the EHR database were utilized (2016-2019). A team of interdisciplinary professionals reviewed ICD codes and individual charts. Through a modified Delphi approach, consensus was reached on determination of medical complexity. We then compared our classification scheme to a commonly utilized Complex Chronic Condition (CCC) with utilization and charges measured over the four-year period among our patient cohort using chi-square tests.

Results: Overall there was 77% agreement between the PPMC and CCC classification scheme in determination of chronic disease status in the period studied. The PPMC identified 86.7% of patients as having no chronic conditions in the cohort, while the CCC scheme identified 78.9% in the baseline year. Less children were identified as having a complex chronic condition status overall in the PPMC scheme. The PPMC identified a greater percentage of patients with persistently elevated health system utilization than the CCC classification scheme over the period studied, as well as those with persistently high utilization and medical charges.Conclusion(s): The PPMC classification scheme is more longitudinally accurate than a previously commonly utilized scheme in identifying CMC with persistently high utilization and charges. This has important implications for population health management as health systems struggle to properly identify and provide needed services to CMC and their families.