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High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program
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 Medic...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8539737/ https://www.ncbi.nlm.nih.gov/pubmed/34686170 http://dx.doi.org/10.1186/s12913-021-07116-6 |
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author | Quinton, Jacob K. Duru, O. Kenrik Jackson, Nicholas Vasilyev, Arseniy Ross-Degnan, Dennis O’Shea, Donna L. Mangione, Carol M. |
author_facet | Quinton, Jacob K. Duru, O. Kenrik Jackson, Nicholas Vasilyev, Arseniy Ross-Degnan, Dennis O’Shea, Donna L. Mangione, Carol M. |
author_sort | Quinton, Jacob K. |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-021-07116-6. |
format | Online Article Text |
id | pubmed-8539737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85397372021-10-25 High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program Quinton, Jacob K. Duru, O. Kenrik Jackson, Nicholas Vasilyev, Arseniy Ross-Degnan, Dennis O’Shea, Donna L. Mangione, Carol M. BMC Health Serv Res Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-021-07116-6. BioMed Central 2021-10-23 /pmc/articles/PMC8539737/ /pubmed/34686170 http://dx.doi.org/10.1186/s12913-021-07116-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Quinton, Jacob K. Duru, O. Kenrik Jackson, Nicholas Vasilyev, Arseniy Ross-Degnan, Dennis O’Shea, Donna L. Mangione, Carol M. High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program |
title | High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program |
title_full | High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program |
title_fullStr | High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program |
title_full_unstemmed | High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program |
title_short | High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program |
title_sort | high-cost high-need patients in medicaid: segmenting the population eligible for a national complex case management program |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8539737/ https://www.ncbi.nlm.nih.gov/pubmed/34686170 http://dx.doi.org/10.1186/s12913-021-07116-6 |
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