Abstract
BackgroundHigh-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.MethodsOur 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.ResultsSix 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%).ConclusionsWe 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.
Highlights
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
High-cost high-need patients have been disaggregated into clinically distinct subgroups or ‘segmented’ at various cost thresholds in many risk-bearing practice settings [5] and integrated health systems [6, 7], as well as at the population level by insurance risk pool including Medicare [8] and Medicare Advantage [9], with the results being incorporated into a national model of high-cost high-need patients [10] and informing value-based payment models in the Medicare program [11]
Study population Our beginning sample consisted of 676,161 Medicaid beneficiaries age 21 and above who were identified as eligible for a complex case management program implemented by UnitedHealthcare (UHC) in 15 states between Jan 1, 2012 and May 1, 2015
Summary
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. The majority of expenditure in any insured group concentrates in a few patients, with 5% of individuals accounting for approximately 50% of expenditure year-to-year [2] The stability of this cost distribution has led to an actuarily important designation of patients as high-cost highneed, defining groups by cost or risk thresholds aggregates clinically distinct subgroups and definitions for high-cost high-need vary [3]. High-cost high-need patients have been disaggregated into clinically distinct subgroups or ‘segmented’ at various cost thresholds in many risk-bearing practice settings [5] and integrated health systems [6, 7], as well as at the population level by insurance risk pool including Medicare [8] and Medicare Advantage [9], with the results being incorporated into a national model of high-cost high-need patients [10] and informing value-based payment models in the Medicare program [11]. Frequently phone-based care coordination programs led by RN case managers [15], have been shown to be effective in Medicare [16] but have been difficult to effectively implement in Medicaid, leading to notable high profile programs with null findings [17], and some small positive demonstrations [18]
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