Incorporating patient self-reported data may allow health systems to more effectively identify specific groups of older adults with unique needs, thereby helping to inform the development of patient-centered, tailored care management approaches. The authors of this paper used data from a Medicare health risk assessment, including perceived health status, emotional well-being, pain, function, falls, and presence of an advance directive, to develop clinically meaningful subgroups using two different analytical methods. The data used, which extend beyond traditional diagnostic and laboratory data, enable segmentation of older adults into subgroups with identifiable care needs.
This resource describes how segmentation approaches using patient-self reported data can uncover subgroups that may be at high risk of incurring high future costs of care, which may not have been identified using only utilization outcomes because they are not currently using significant health care resources. Health systems may use these methods for population-level care design.