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Patterns of Morbidity Across the Lifespan: A Population Segmentation Framework for Classifying Health Care Needs for All Ages.
Lemke, Klaus W; Forrest, Christopher B; Leff, Bruce A; Boyd, Cynthia M; Gudzune, Kimberly A; Pollack, Craig E; Pandya, Chintan J; Weiner, Jonathan P.
Afiliação
  • Lemke KW; Center for Population Health Informatics.
  • Forrest CB; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
  • Leff BA; Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
  • Boyd CM; Department of Medicine, Johns Hopkins University School of Medicine.
  • Gudzune KA; Department of Medicine, Johns Hopkins University School of Medicine.
  • Pollack CE; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
  • Pandya CJ; Department of Medicine, Johns Hopkins University School of Medicine.
  • Weiner JP; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Medical Institutions.
Med Care ; 2023 Nov 07.
Article em En | MEDLINE | ID: mdl-37962403
ABSTRACT

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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Med Care Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Med Care Ano de publicação: 2023 Tipo de documento: Article