Improving care for older adults: a model to segment the senior population.

CONTEXT

Risk stratification and tailored interventions are key population-level care management strategies among older adults, whose needs range from screening and prevention to end-of-life care.

OBJECTIVE

To validate the Senior Segmentation Algorithm, a tool using administrative and clinical data from the electronic health record to identify each member aged 65 years and older as belonging to 1 of 4 Care Groups with similar needs: those without chronic conditions, with one or more chronic conditions, with advanced illness or end-organ failure, or with extreme frailty or nearing the end of life.

DESIGN

Multiple validation methods.

MAIN OUTCOME MEASURES

Concordance with physician judgment, stability of segmentation over time, convergence with mortality, hospitalization, and readmission rates, and costs of care.

RESULTS

Concordance of the algorithm with physician-assessed segmentation of 1615 Medicare recipients was 85%. After 1 year, approximately 85% of 86,140 surviving seniors remained in the same care group; 3.9% moved to a lower need group; and 11% moved to a higher need group. Six-month and 12-month mortality rates varied substantially across care groups. The algorithm performed similarly to the likelihood of hospitalization score in predicting hospitalization and readmissions.

CONCLUSIONS

The Senior Segmentation Algorithm accurately identifies older adults in care groups with similar needs, trajectories, and utilization patterns. It is being implemented in all Kaiser Permanente Regions, with the goal of determining key elements of care for members in each group. In addition, future efforts will aim to slow progression to higher need care groups and to identify necessary improvements in delivery system design.