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How to Classify Super-Utilizers: A Methodological Review of Super-Utilizer Criteria Applied to the Utah Medicaid Population, 2016–2017
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4 2020
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Source: Popul Health Manag. 23(2):165-173
[PDF-482.07 KB]
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Alternative Title:Popul Health Manag
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Description:A limited number of patients, commonly termed super-utilizers, account for the bulk of health care expenditures. Multiple criteria for identifying super-utilizers exist, but no standard methodology is available for determining which criteria should be used for a specific population. Application is often arbitrary, and poorly aligned super-utilizer criteria might result in misallocation of resources and diminished effects of interventions. This study sought to apply an innovative, data-driven approach to classify super-utilizers among Utah Medicaid beneficiaries. The authors conducted a literature review of research methods to catalogue applied super-utilizer criteria. The most commonly used criteria were applied to Utah Medicaid beneficiaries enrolled during July 1, 2016-June 30, 2017, using their previous 12 months of claims data (N = 309,921). The |-medoids algorithm cluster analysis was used to find groups of beneficiaries with similar characteristic based on criteria from the literature. In all, 180 super-utilizer criteria were identified in the literature, 21 of which met the inclusion criteria. When these criteria were applied to Utah Medicaid data, 5 distinct subpopulation clusters were found: non-super-utilizers (n = 163,118), beneficiaries with multiple chronic or mental health conditions (n = 68,054), beneficiaries with a single chronic health condition (n = 43,939), emergency department super-utilizers with chronic or mental health conditions (n = 7809), and beneficiaries with uncomplicated hospitalizations (n = 27,001). This study demonstrates how cluster analysis can aid in selecting characteristics from the literature that systematically differentiate super-utilizer groups from other beneficiaries. This methodology might be useful to health care systems for identifying super-utilizers within their patient populations.
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Pubmed ID:31424319
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Pubmed Central ID:PMC8995378
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Volume:23
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Issue:2
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