| Autor: |
Jacob K. Quinton, O. Kenrik Duru, Nicholas Jackson, Arseniy Vasilyev, Dennis Ross-Degnan, Donna L. O’Shea, Carol M. Mangione |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
BMC Health Services Research, Vol 21, Iss 1, Pp 1-8 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1472-6963 |
| DOI: |
10.1186/s12913-021-07116-6 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
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