Patterns of Morbidity Across the Lifespan
A Population Segmentation Framework for Classifying Health Care Needs for All Ages
Bibliographic Data
BACKGROUND: 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. OBJECTIVE: To describe and empirically apply a new clinically relevant population segmentation framework applicable to all payers and all ages across the lifespan. RESEARCH DESIGN AND SUBJECTS: 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. MEASURES: 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. RESULTS: 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. CONCLUSIONS: 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
Environmental health · Family medicine · Health care · Medicaid · Population · Chronic Disease Management Strategies · Machine Learning in Healthcare · Medicine · Primary Care and Health Outcomes · Gerontology
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