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Expenditure variations analysis using residuals for identifying high health care utilizers in a state Medicaid program
BACKGROUND: High utilizers receive great attention in health care research because they have a largely disproportionate spending. Existing analyses usually identify high utilizers with an empirical threshold on the number of health care visits or associated expenditures. However, such count-and-cost...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6626330/ https://www.ncbi.nlm.nih.gov/pubmed/31299965 http://dx.doi.org/10.1186/s12911-019-0870-4 |
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author | Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Ranka, Sanjay |
author_facet | Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Ranka, Sanjay |
author_sort | Yang, Chengliang |
collection | PubMed |
description | BACKGROUND: High utilizers receive great attention in health care research because they have a largely disproportionate spending. Existing analyses usually identify high utilizers with an empirical threshold on the number of health care visits or associated expenditures. However, such count-and-cost based criteria might not be best for identifying impactable high utilizers. METHODS: We propose an approach to identify impactable high utilizers using residuals from regression-based health care utilization risk adjustment models to analyze the variations in health care expenditures. We develop linear and tree-based models to best adjust per-member per-month health care cost by clinical and socioeconomic risk factors using a large administrative claims dataset from a state public insurance program. RESULTS: The risk adjustment models identify a group of patients with high residuals whose demographics and categorization of comorbidities are similar to other patients but who have a significant amount of unexplained health care utilization. Deeper analysis of the essential hypertension cohort and chronic kidney disease cohort shows these variations in expenditures could be within individual ICD-9-CM codes and from different mixtures of ICD-9-CM codes. Additionally, correlation analysis with 3M™ Potentially Preventable Events (PPE) software shows that a portion of this utilization may be preventable. In addition, the high utilizers persist from year to year. CONCLUSIONS: After risk adjustment, patients with higher than expected expenditures (high residuals) are associated with more potentially preventable events. These residuals are temporally consistent and hence may be useful in identifying and intervening impactable high utilizers. |
format | Online Article Text |
id | pubmed-6626330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66263302019-07-23 Expenditure variations analysis using residuals for identifying high health care utilizers in a state Medicaid program Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Ranka, Sanjay BMC Med Inform Decis Mak Research Article BACKGROUND: High utilizers receive great attention in health care research because they have a largely disproportionate spending. Existing analyses usually identify high utilizers with an empirical threshold on the number of health care visits or associated expenditures. However, such count-and-cost based criteria might not be best for identifying impactable high utilizers. METHODS: We propose an approach to identify impactable high utilizers using residuals from regression-based health care utilization risk adjustment models to analyze the variations in health care expenditures. We develop linear and tree-based models to best adjust per-member per-month health care cost by clinical and socioeconomic risk factors using a large administrative claims dataset from a state public insurance program. RESULTS: The risk adjustment models identify a group of patients with high residuals whose demographics and categorization of comorbidities are similar to other patients but who have a significant amount of unexplained health care utilization. Deeper analysis of the essential hypertension cohort and chronic kidney disease cohort shows these variations in expenditures could be within individual ICD-9-CM codes and from different mixtures of ICD-9-CM codes. Additionally, correlation analysis with 3M™ Potentially Preventable Events (PPE) software shows that a portion of this utilization may be preventable. In addition, the high utilizers persist from year to year. CONCLUSIONS: After risk adjustment, patients with higher than expected expenditures (high residuals) are associated with more potentially preventable events. These residuals are temporally consistent and hence may be useful in identifying and intervening impactable high utilizers. BioMed Central 2019-07-12 /pmc/articles/PMC6626330/ /pubmed/31299965 http://dx.doi.org/10.1186/s12911-019-0870-4 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Ranka, Sanjay Expenditure variations analysis using residuals for identifying high health care utilizers in a state Medicaid program |
title | Expenditure variations analysis using residuals for identifying high health care utilizers in a state Medicaid program |
title_full | Expenditure variations analysis using residuals for identifying high health care utilizers in a state Medicaid program |
title_fullStr | Expenditure variations analysis using residuals for identifying high health care utilizers in a state Medicaid program |
title_full_unstemmed | Expenditure variations analysis using residuals for identifying high health care utilizers in a state Medicaid program |
title_short | Expenditure variations analysis using residuals for identifying high health care utilizers in a state Medicaid program |
title_sort | expenditure variations analysis using residuals for identifying high health care utilizers in a state medicaid program |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6626330/ https://www.ncbi.nlm.nih.gov/pubmed/31299965 http://dx.doi.org/10.1186/s12911-019-0870-4 |
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