High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program

Abstract

Background High-cost high-need patients are typically defined by risk or cost thresholds which aggregate clinically diverse subgroups into a single ‘high-need high-cost’ designation. Programs have had limited success in reducing utilization or improving quality of care for high-cost high-need Medicaid patients, which may be due to the underlying clinical heterogeneity of patients meeting high-cost high-need designations. Methods Our objective was to segment a population of high-cost high-need Medicaid patients (N = 676,161) eligible for a national complex case management program between January 2012 and May 2015 to disaggregate clinically diverse subgroups. Patients were eligible if they were in the top 5 % of annual spending among UnitedHealthcare Medicaid beneficiaries. We used k-means cluster analysis, identified clusters using an information-theoretic approach, and named clusters using the patients’ pattern of acute and chronic conditions. We assessed one-year overall and preventable hospitalizations, overall and preventable emergency department (ED) visits, and cluster stability. Results Six clusters were identified which varied by utilization and stability. The characteristic condition patterns were: 1) pregnancy complications, 2) behavioral health, 3) relatively few conditions, 4) cardio-metabolic disease, and complex illness with relatively 5) low or 6) high resource use. The patients varied by cluster by average ED visits (2.3–11.3), hospitalizations (0.3–2.0), and cluster stability (32–91%). Conclusions We concluded that disaggregating subgroups of high-cost high-need patients in a large multi-state Medicaid sample identified clinically distinct clusters of patients who may have unique clinical needs. Segmenting previously identified high-cost high-need populations thus may be a necessary strategy to improve the effectiveness of complex case management programs in Medicaid.

Characterizing hospitalization trajectories in the high-need, high-cost population using electronic health record data

“…Finally, a prototypical low utilizer may be a patient with psychiatric disease but no significant medical comorbidities. That this picture contrasts with prior evidence indicating high psychiatric and substance use burden and low socioeconomic status as key risk factors for ED utilization 2 , 4 may be due to different underlying processes driving ED vs inpatient utilization and/or methodological differences such as our study's focus on incident rather than prevalent high utilization.…”

“…Recent research has attempted to unravel this heterogeneity, using administrative and claims data to identify distinct clusters within the HNHC population with respect to sociodemographic and clinical characteristics. 4-10 However, despite these advances, significant gaps persist in our understanding of (1) how high utilization first emerges and (2) why some patients become persistently high utilizers, whereas others’ utilization is high only transiently. 2 These gaps highlight the importance of focusing not only on high utilization in a cross-sectional, aggregate sense (eg, total hospitalizations in a given year) but also in terms of longitudinal patterns, or trajectories, of utilization and risk that evolve dynamically over time, as has recently been studied in other contexts.…”

Identifying High-Cost, High-Need Patients in a Network of Community Hospitals

“…Major complex chronic (MCC) is defined as ≥2 of the top 10 patient diagnoses, per high-cost group including congestive heart failure, vascular disease, chronic obstructive pulmonary disease, specified heart arrhythmias, renal failure, diabetes without complication, cardiorespiratory failure and shock, polyneuropathy, major complications of medical care and trauma, and angina pectoris/old myocardial infarction. 14 …”

“…16 , 17 In this study, a community-based health system comprised of 5 hospitals serving disparate communities used the NAM framework to, 13 , 16 (1) identify the high-cost patient population and (2) segment high-need patient populations with major complex chronic conditions within the high-cost category. 14 , 17 Examining the demographics and needs of the HCHN population can shed light on affected individuals and guide strategic interventions to improve patient lives and reduce the cost burden.…”

Does Interdisciplinary Care Team Care Management Improve Health Quality and Demonstrate Cost-Effectiveness?

“…Payers and providers struggling to manage the rising costs of care have intervened with a variety of strategies targeting complex, high-needs patients in the hope that these efforts will prevent costly and preventable utilization. A variety of programs have been employed in this respect, including medical homes, disease and case management programs, transitional care programs, and others (Price-Haywood et al, 2018; Quinton et al, 2021). Similarly, these programs have employed a variety of strategies for identifying patients for program inclusion, ranging from selection based on provider referral to selection using complex data-driven algorithms (Hong et al, 2014).…”