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Challenges in predicting future high-cost patients for care management interventions

BACKGROUND: To test the accuracy of a segmentation approach using claims data to predict Medicare beneficiaries most likely to be hospitalized in a subsequent year. METHODS: This article uses a 100-percent sample of Medicare beneficiaries from 2017 to 2018. This analysis is designed to illustrate th...

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Autores principales: Crowley, Chris, Perloff, Jennifer, Stuck, Amy, Mechanic, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503094/
https://www.ncbi.nlm.nih.gov/pubmed/37710262
http://dx.doi.org/10.1186/s12913-023-09957-9
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author Crowley, Chris
Perloff, Jennifer
Stuck, Amy
Mechanic, Robert
author_facet Crowley, Chris
Perloff, Jennifer
Stuck, Amy
Mechanic, Robert
author_sort Crowley, Chris
collection PubMed
description BACKGROUND: To test the accuracy of a segmentation approach using claims data to predict Medicare beneficiaries most likely to be hospitalized in a subsequent year. METHODS: This article uses a 100-percent sample of Medicare beneficiaries from 2017 to 2018. This analysis is designed to illustrate the actuarial limitations of person-centered risk segmentation by looking at the number and rate of hospitalizations for progressively narrower segments of heart failure patients and a national fee-for-service comparison group. Cohorts are defined using 2017 data and then 2018 hospitalization rates are shown graphically. RESULTS: As the segments get narrower, the 2018 hospitalization rates increased, but the percentage of total Medicare FFS hospitalizations accounted for went down. In all three segments and the total Medicare FFS population, more than half of all patients did not have a hospitalization in 2018. CONCLUSIONS: With the difficulty of identifying future high utilizing beneficiaries, health systems should consider the addition of clinician input and ‘light touch’ monitoring activities to improve the prediction of high-need, high-cost cohorts. It may also be beneficial to develop systemic strategies to manage utilization and steer beneficiaries to efficient providers rather than targeting individual patients.
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spelling pubmed-105030942023-09-16 Challenges in predicting future high-cost patients for care management interventions Crowley, Chris Perloff, Jennifer Stuck, Amy Mechanic, Robert BMC Health Serv Res Research BACKGROUND: To test the accuracy of a segmentation approach using claims data to predict Medicare beneficiaries most likely to be hospitalized in a subsequent year. METHODS: This article uses a 100-percent sample of Medicare beneficiaries from 2017 to 2018. This analysis is designed to illustrate the actuarial limitations of person-centered risk segmentation by looking at the number and rate of hospitalizations for progressively narrower segments of heart failure patients and a national fee-for-service comparison group. Cohorts are defined using 2017 data and then 2018 hospitalization rates are shown graphically. RESULTS: As the segments get narrower, the 2018 hospitalization rates increased, but the percentage of total Medicare FFS hospitalizations accounted for went down. In all three segments and the total Medicare FFS population, more than half of all patients did not have a hospitalization in 2018. CONCLUSIONS: With the difficulty of identifying future high utilizing beneficiaries, health systems should consider the addition of clinician input and ‘light touch’ monitoring activities to improve the prediction of high-need, high-cost cohorts. It may also be beneficial to develop systemic strategies to manage utilization and steer beneficiaries to efficient providers rather than targeting individual patients. BioMed Central 2023-09-14 /pmc/articles/PMC10503094/ /pubmed/37710262 http://dx.doi.org/10.1186/s12913-023-09957-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Crowley, Chris
Perloff, Jennifer
Stuck, Amy
Mechanic, Robert
Challenges in predicting future high-cost patients for care management interventions
title Challenges in predicting future high-cost patients for care management interventions
title_full Challenges in predicting future high-cost patients for care management interventions
title_fullStr Challenges in predicting future high-cost patients for care management interventions
title_full_unstemmed Challenges in predicting future high-cost patients for care management interventions
title_short Challenges in predicting future high-cost patients for care management interventions
title_sort challenges in predicting future high-cost patients for care management interventions
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503094/
https://www.ncbi.nlm.nih.gov/pubmed/37710262
http://dx.doi.org/10.1186/s12913-023-09957-9
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