One size does not fit all: An examination of Medicare beneficiary differences across multiple patient segmentation tools

background: With over 55 million Medicare beneficiaries, a number steadily growing, there is a critical need to better understand the sociodemographic characteristics, knowledge, and attitudes of different segments of this population.  Patient segmentation is one strategy used to define subgroups among the larger beneficiary population, by using the principles of cluster analysis to identify and group individuals with similar characteristics. We used multiple segmentation models and examined characteristic differences between patient clusters using each approach.

methods: We held a series of 13 Mobile Clinics in the Fall of 2014 during which beneficiaries were provided Medicare Part D plan assistance, medication therapy management, and health care screenings/services. We conducted structured beneficiary interviews during which sociodemographic, knowledge and attitudinal behaviors were recorded. Beneficiary answers to specific questions were used to populate different segmentation algorithms. The Williams & Heller Medicare Screening Segmentation Screening Tool (SST) results in four segments (“Active,” “Passive,” “High Effort,” and “Complacent”). The CMS Audience Segmentation Model (CMS Model) results in six segments (“Informed, Healthy & Educated,” “Sick, Active & Worried,” “Mature & Secure,” “Healthy & Young,” “Passive & Skeptical,” and “Vulnerable & Unengaged”). Beneficiary knowledge of the Part D benefit, confidence in picking a Part D plan, reason(s) for attending the clinic, and sociodemographic characteristics were examined between segments for each model.

results: Of the 713 beneficiaries that could be classified according to the SST Tool, 263 (36.9%), 167 (23.4%), 155 (21.7%), and 128 (18.0%) fell into the Active, Complacent, High Effort and Passive groups, respectively. In comparison, 842 beneficiaries were divided into one of the CMS Model segments. The majority, 482 (57.2%), were in the Mature and Secure segment, followed by 151 (18.1%) in the Vulnerable and Unengaged, and 131 (15.5%) in the Sick, Active and Worried segments. The remaining 77 (9.1%) beneficiaries were distributed relatively equally between the other three segments. Significant differences in knowledge and understanding of the Part D benefit, type of Medicare drug plan, race, income/subsidy status, education, and difficulty in paying for medications was found between segments in both models.

conclusions: Patient segmentation may help us better understand beneficiary knowledge, attitude and behavioral differences. Furthermore, it can help inform advocates on how to develop effective outreach and tailored educational materials so that beneficiaries can fully take advantage of their Medicare benefits.