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

Richard W Grant,Jodi Mccloskey,Elizabeth Bayliss,Connie Uratsu,Chris J Kennedy,Meghan Hatfield,James D Ralston

doi:10.1001/jamanetworkopen.2020.29068

Abstract

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. To define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters. 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 of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles. Complex patient profiles, 1-year health care utilization, and mortality outcomes by profile. The analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management. The findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.

Highlights

A small number of patients consume a large proportion of the total national health care budget.[1]
The findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions
Meaning The findings suggest that a single care model may not meet the needs of adults with high comorbidity and care utilization

Summary

Introduction

A small number of patients consume a large proportion of the total national health care budget.[1]. One recognized limitation of current care programs involves the initial step of patient identification.[11] Individuals with complex medical and social care needs have traditionally been identified by prior year costs or care utilization, number and type of concurrent comorbid conditions, and/or predicted future hospitalization or costs. These approaches lack specificity and may contribute to the poor results seen for most prior care management interventions. There are limited empirical data to guide which patient subgroups exist within the overall medically complex patient population

Methods

Results

Discussion

Conclusion