Michael J. Durfee

Background: Patients with repeated hospitalizations represent a group with potentially avoidable utilization. Recent publications have begun to highlight the heterogeneity of this group. Latent class analysis provides a novel methodological approach to utilizing administrative data to identify clinically meaningful subgroups of patients to inform tailored intervention efforts. Objective: The objective of the study was to identify clinically distinct subgroups of adult superutilizers. Research Design: Retrospective cohort analysis. Subjects: Adult patients who had an admission at an urban safety-net hospital in 2014 and 2 or more admissions within the preceding 12 months. Measures: Patient-level medical, mental health (MH) and substance use diagnoses, social characteristics, demographics, utilization and charges were obtained from administrative data. Latent class analyses were used to determine the number and characteristics of latent subgroups that best represented these data. Results: In this cohort (N=1515), a 5-class model was preferred based on model fit indices, clinical interpretability and class size: class 1 (16%) characterized by alcohol use disorder and homelessness; class 2 (14%) characterized by medical conditions, MH/substance use disorders and homelessness; class 3 (25%) characterized primarily by medical conditions; class 4 (13%) characterized by more serious MH disorders, drug use disorder and homelessness; and class 5 (32%) characterized by medical conditions with some MH and substance use. Patient demographics, utilization, charges and mortality also varied by class. Conclusions: The overall cohort had high rates of multiple chronic medical conditions, MH, substance use disorders, and homelessness. However, the patterns of these conditions were different between subgroups, providing important information for tailoring interventions.

Context: The Center for Medicare and Medicaid Innovation (CMMI) awarded Denver Health's (DH) integrated, safety net health care system $19.8 million to implement a “population health” approach into the delivery of primary care. This major practice transformation builds on the Patient Centered Medical Home (PCMH) and Wagner’s Chronic Care Model (CCM) to achieve the “Triple Aim”: improved health for populations, care to individuals, and lower per capita costs.Case description: This paper presents a case study of how DH integrated published predictive models and front-line clinical judgment to implement a clinically actionable, risk stratification of patients. This population segmentation approach was used to deploy enhanced care team staff resources and to tailor care-management services to patient need, especially for patients at high risk of avoidable hospitalization. Developing, implementing, and gaining clinical acceptance of the Health Information Technology (HIT) solution for patient risk stratification was a major grant objective.Findings: In addition to describing the Information Technology (IT) solution itself, we focus on the leadership and organizational processes that facilitated its multidisciplinary development and ongoing iterative refinement, including the following: team composition, target population definition, algorithm rule development, performance assessment, and clinical-workflow optimization. We provide examples of how dynamic business intelligence tools facilitated clinical accessibility for program design decisions by enabling real-time data views from a population perspective down to patient-specific variables.Conclusions: We conclude that population segmentation approaches that integrate clinical perspectives with predictive modeling results can better identify high opportunity patients amenable to medical home-based, enhanced care team interventions.