Patterns of Morbidity Across the Lifespan: A Population Segmentation Framework for Classifying Health Care Needs for...

Classification systems to segment such patients into subgroups for purposes of care management and population analytics should balance administrative simplicity with clinical meaning and measurement precision.

To describe and empirically apply a new clinically relevant population segmentation framework applicable to all payers and all ages across the lifespan.

Cross-sectional analyses using insurance claims database for 3.31 Million commercially insured and 1.05 Million Medicaid enrollees under 65 years old; and 5.27 Million Medicare fee-for-service beneficiaries aged 65 and older.

The "Patient Need Groups" (PNGs) framework, we developed, classifies each person within the entire 0-100+ aged population into one of 11 mutually exclusive need-based categories. For each PNG segment, we documented a range of clinical and resource endpoints, including health care resource use, avoidable emergency department visits, hospitalizations, behavioral health conditions, and social need factors.

The PNG categories included: (1) nonuser; (2) low-need child; (3) low-need adult; (4) low-complexity multimorbidity; (5) medium-complexity multimorbidity; (6) low-complexity pregnancy; (7) high-complexity pregnancy; (8) dominant psychiatric/behavioral condition; (9) dominant major chronic condition; (10) high-complexity multimorbidity; and (11) frailty. Each PNG evidenced a characteristic age-related trajectory across the full lifespan. In addition to offering clinically cogent groupings, large percentages (29%-62%) of patients in two pregnancy and high-complexity multimorbidity and frailty PNGs were in a high-risk subgroup (upper 10%) of potential future health care utilization.

The PNG population segmentation approach represents a comprehensive measurement framework that captures and categorizes available electronic health care data to characterize individuals of all ages based on their needs.