papers

Clinical features of high-risk older persons identified by predictive modeling

Dis Manag 9:56-62.

Johns Hopkins HealthCare, Glen Burnie, MD, USA

The objective of this study was to describe the clinical features of older persons identified as high risk by a predictive modeling algorithm and to determine their suitability for clinical interventions like case management or disease management. A cross-sectional survey was undertaken at a community-based general internal medicine practice with 826 older patients enrolled in a Medicare-like health plan for military retirees and their dependents. Administrative claims data provided information about all 826 enrollees’ chronic conditions, their use of health services, and the cost of those services during the past year. A survey mailed to 150 identified high-risk enrollees provided information about sociodemographic characteristics, general health, bed disability days, restricted activity days, activities of daily living (ADL) limitations, and instrumental activities of daily living (IADL) limitations. Compared to the 676 low-risk enrollees, the 150 high-risk enrollees had higher prevalence of eight individual chronic conditions, higher total chronic conditions (2.93 vs. 1.48, p < 0.001), higher annual rates of hospital admission (1.1 vs. 0.1, p 0.001), more annual hospital days (7.3 vs. 0.5, p 0.001), and higher total health insurance expendiures ($22,815 vs. $3,726, p 0.001). The high-risk respondents to the survey (response rate = 80.0%) had uboptimal health (42.8% “fair or poor”), impaired functional ability (36.3% with 1+ ADL limitations, 58.1% with 1+ IADL limitations), and frequent health-related disruptions in their activities during the previous six months (38.7% with 1+ bed disability day, 52.3% with 1+ restricted activity day). A claims-based predictive modeling algorithm identifies older persons whose health, functional ability, and use of health services suggest they are good candidates for clinical interventions such as case management and disease management.

PMID: 16466342