Cluster Analysis of the Highest Users of Medical, Behavioral Health, and Social Services in San Francisco.

Autor: Hewlett MM; Department of Emergency Medicine, University of California, San Francisco, San Francisco, CA, USA. Meghan.hewlett@ucsf.edu., Raven MC; Department of Emergency Medicine, University of California, San Francisco, San Francisco, CA, USA.; Benioff Homelessness and Housing Initiative, University of California, San Francisco, San Francisco, USA., Graham-Squire D; Benioff Homelessness and Housing Initiative, University of California, San Francisco, San Francisco, USA., Evans JL; Benioff Homelessness and Housing Initiative, University of California, San Francisco, San Francisco, USA., Cawley C; Department of Emergency Medicine, University of California, San Francisco, San Francisco, CA, USA.; Benioff Homelessness and Housing Initiative, University of California, San Francisco, San Francisco, USA., Kushel M; Benioff Homelessness and Housing Initiative, University of California, San Francisco, San Francisco, USA.; Center for Vulnerable Populations, University of California, San Francisco, USA., Kanzaria HK; Department of Emergency Medicine, University of California, San Francisco, San Francisco, CA, USA.; Benioff Homelessness and Housing Initiative, University of California, San Francisco, San Francisco, USA.; Center for Vulnerable Populations, University of California, San Francisco, USA.
Jazyk: angličtina
Zdroj: Journal of general internal medicine [J Gen Intern Med] 2023 Apr; Vol. 38 (5), pp. 1143-1151. Date of Electronic Publication: 2022 Nov 29.
DOI: 10.1007/s11606-022-07873-y
Abstrakt: Background: In the City and County of San Francisco, frequent users of emergent and urgent services across different settings (i.e., medical, mental health (MH), substance use disorder (SUD) services) are referred to as high users of multiple systems (HUMS). While often grouped together, frequent users of the health care system are likely a heterogenous population composed of subgroups with differential management needs.
Objective: To identify subgroups within this HUMS population using a cluster analysis.
Design: Cross-sectional study of HUMS patients for the 2019-2020 fiscal year using the Coordinated Care Management System (CCMS), San Francisco Department of Public Health's integrated data system.
Participants: We calculated use scores based on nine types of urgent and emergent medical, MH, and SUD services and identified the top 5% of HUMS patients. Through k-medoids cluster analysis, we identified subgroups of HUMS patients.
Main Measures: Subgroup-specific demographic, comorbidity, and service use profiles.
Key Results: The top 5% of HUMS patients in the study period included 2657 individuals; 69.7% identified as men and 66.5% identified as non-White. We detected 5 subgroups: subgroup 1 (N = 298, 11.2%) who were relatively younger with prevalent MH and SUD comorbidities, and MH services use; subgroup 2 (N = 478, 18.0%), who were experiencing homelessness, with multiple comorbidities, and frequent use of medical services; subgroup 3 (N = 449, 16.9%), who disproportionately self-identified as Black, with prolonged homelessness, multiple comorbidities, and persistent HUMS status; subgroup 4 (N = 690, 26.0%), who were relatively older, disproportionately self-identified as Black, with prior homelessness, multiple comorbidities, and frequent use of medical services; and subgroup 5 (N=742, 27.9%), who disproportionately self-identified as Latinx, were housed, with medical comorbidities and frequent medical service use.
Conclusions: Our study highlights the heterogeneity of HUMS patients. Interventions must be tailored to meet the needs of these diverse patient subgroups.
(© 2022. The Author(s), under exclusive licence to Society of General Internal Medicine.)
Databáze: MEDLINE
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