Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles.

ABSTRACT:

Importance

Medically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date.

Objective

To define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters.

Design, setting, and participants

This cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point

SUBMITTER: Grant RW 

PROVIDER: S-EPMC7733156 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature