OBJECTIVE: To examine the heterogeneity of home care clients who use rehabilitation services by using the K-means algorithm to identify previously unknown patterns of clinical characteristics. DESIGN: Observational study of secondary data. SETTING: Home care system. PARTICIPANTS: Assessment information was collected on 150,253 home care clients using the provincially mandated Resident Assessment Instrument-Home Care (RAI-HC) data system. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Assessment information from every long-stay (>60 d) home care client that entered the home care system between 2005 and 2008 and used rehabilitation services within 3 months of their initial assessment was analyzed. The K-means clustering algorithm was applied using 37 variables from the RAI-HC assessment. RESULTS: The K-means cluster analysis resulted in the identification of 7 relatively homogeneous subgroups that differed on characteristics such as age, sex, cognition, and functional impairment. Client profiles were created to illustrate the diversity of this geriatric population. CONCLUSIONS: The K-means algorithm provided a useful way to segment a heterogeneous rehabilitation client population into more homogeneous subgroups. This analysis provides an enhanced understanding of client characteristics and needs, and could enable more appropriate targeting of rehabilitation services for home care clients.
OBJECTIVE: To examine the heterogeneity of home care clients who use rehabilitation services by using the K-means algorithm to identify previously unknown patterns of clinical characteristics. DESIGN: Observational study of secondary data. SETTING: Home care system. PARTICIPANTS: Assessment information was collected on 150,253 home care clients using the provincially mandated Resident Assessment Instrument-Home Care (RAI-HC) data system. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Assessment information from every long-stay (>60 d) home care client that entered the home care system between 2005 and 2008 and used rehabilitation services within 3 months of their initial assessment was analyzed. The K-means clustering algorithm was applied using 37 variables from the RAI-HC assessment. RESULTS: The K-means cluster analysis resulted in the identification of 7 relatively homogeneous subgroups that differed on characteristics such as age, sex, cognition, and functional impairment. Client profiles were created to illustrate the diversity of this geriatric population. CONCLUSIONS: The K-means algorithm provided a useful way to segment a heterogeneous rehabilitation client population into more homogeneous subgroups. This analysis provides an enhanced understanding of client characteristics and needs, and could enable more appropriate targeting of rehabilitation services for home care clients.
Authors: Caitlin McArthur; Luke A Turcotte; Chi-Ling Joanna Sinn; Katherine Berg; John N Morris; John P Hirdes Journal: J Am Med Dir Assoc Date: 2022-04-18 Impact factor: 7.802