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Drivers of High-cost Medical Complexity in a Medicaid Population

Efforts to improve outcomes for the 10% of patients using two thirds of health care expenditures increasingly include addressing social determinants. Empiric evidence is needed to identify the highest impact nonmedical drivers of medical complexity and cost. OBJECTIVES: This study examines whether c...

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Autores principales: Labby, David, Wright, Bill, Broffman, Lauren, Holtorf, Megan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017939/
https://www.ncbi.nlm.nih.gov/pubmed/31876646
http://dx.doi.org/10.1097/MLR.0000000000001261
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author Labby, David
Wright, Bill
Broffman, Lauren
Holtorf, Megan
author_facet Labby, David
Wright, Bill
Broffman, Lauren
Holtorf, Megan
author_sort Labby, David
collection PubMed
description Efforts to improve outcomes for the 10% of patients using two thirds of health care expenditures increasingly include addressing social determinants. Empiric evidence is needed to identify the highest impact nonmedical drivers of medical complexity and cost. OBJECTIVES: This study examines whether complex, highest cost patients have different patterns of critical life adversity than those with better health and lower utilization. RESEARCH DESIGN: Using a validated algorithm we constructed a complexity/cost risk patient profile. We developed and fielded a life experience survey (Supplemental Digital Content 1, http://links.lww.com/MLR/B920) to a representative sample, then examined how the prevalence of specific adversities varied between complex, high-cost individuals, and others. SUBJECTS: Surveys were sent to 9176 adult Medicaid members in Portland, Oregon. MEASURES: Our primary variable was high medical complexity health cost risk; an alternative specification combined health cost risk and actual utilization/cost. Our survey instrument measured exposure to early and later-life adversities. RESULTS: Compared with healthy individuals in our population, medically complex individuals had significantly higher rates of adversity. The greatest risk of medical complexity and cost was associated with substance use [odds ratio (OR), 4.1], homelessness (OR, 3.0), childhood maltreatment (OR, 2.8), and incarceration (OR 2.4). Those with the highest prior year acute care utilization and cost had the highest rates of these same factors: substance use (62.5%), homelessness (61.7%), childhood maltreatment (55.5%), and incarceration (52.1%). CONCLUSION: Clinical and policy strategies that mitigate high-impact social drivers of poor outcomes are likely critical for improving both health and costs for complex, high-needs patients.
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spelling pubmed-70179392020-03-10 Drivers of High-cost Medical Complexity in a Medicaid Population Labby, David Wright, Bill Broffman, Lauren Holtorf, Megan Med Care Original Articles Efforts to improve outcomes for the 10% of patients using two thirds of health care expenditures increasingly include addressing social determinants. Empiric evidence is needed to identify the highest impact nonmedical drivers of medical complexity and cost. OBJECTIVES: This study examines whether complex, highest cost patients have different patterns of critical life adversity than those with better health and lower utilization. RESEARCH DESIGN: Using a validated algorithm we constructed a complexity/cost risk patient profile. We developed and fielded a life experience survey (Supplemental Digital Content 1, http://links.lww.com/MLR/B920) to a representative sample, then examined how the prevalence of specific adversities varied between complex, high-cost individuals, and others. SUBJECTS: Surveys were sent to 9176 adult Medicaid members in Portland, Oregon. MEASURES: Our primary variable was high medical complexity health cost risk; an alternative specification combined health cost risk and actual utilization/cost. Our survey instrument measured exposure to early and later-life adversities. RESULTS: Compared with healthy individuals in our population, medically complex individuals had significantly higher rates of adversity. The greatest risk of medical complexity and cost was associated with substance use [odds ratio (OR), 4.1], homelessness (OR, 3.0), childhood maltreatment (OR, 2.8), and incarceration (OR 2.4). Those with the highest prior year acute care utilization and cost had the highest rates of these same factors: substance use (62.5%), homelessness (61.7%), childhood maltreatment (55.5%), and incarceration (52.1%). CONCLUSION: Clinical and policy strategies that mitigate high-impact social drivers of poor outcomes are likely critical for improving both health and costs for complex, high-needs patients. Lippincott Williams & Wilkins 2020-03 2019-12-23 /pmc/articles/PMC7017939/ /pubmed/31876646 http://dx.doi.org/10.1097/MLR.0000000000001261 Text en Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/) (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Original Articles
Labby, David
Wright, Bill
Broffman, Lauren
Holtorf, Megan
Drivers of High-cost Medical Complexity in a Medicaid Population
title Drivers of High-cost Medical Complexity in a Medicaid Population
title_full Drivers of High-cost Medical Complexity in a Medicaid Population
title_fullStr Drivers of High-cost Medical Complexity in a Medicaid Population
title_full_unstemmed Drivers of High-cost Medical Complexity in a Medicaid Population
title_short Drivers of High-cost Medical Complexity in a Medicaid Population
title_sort drivers of high-cost medical complexity in a medicaid population
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017939/
https://www.ncbi.nlm.nih.gov/pubmed/31876646
http://dx.doi.org/10.1097/MLR.0000000000001261
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