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Health-weighted Composite Quality Metrics Offer Promise to Improve Health Outcomes in a Learning Health System
Health system leaders sometimes adopt quality metrics without robust supporting evidence of improvements in quality and/or quantity of life, which may impair rather than facilitate improved health outcomes. In brief, there is now no easy way to measure how much “health” is conferred by a health syst...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AcademyHealth
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4371421/ https://www.ncbi.nlm.nih.gov/pubmed/25848572 http://dx.doi.org/10.13063/2327-9214.1022 |
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author | Braithwaite, Scott Stine, Nicholas |
author_facet | Braithwaite, Scott Stine, Nicholas |
author_sort | Braithwaite, Scott |
collection | PubMed |
description | Health system leaders sometimes adopt quality metrics without robust supporting evidence of improvements in quality and/or quantity of life, which may impair rather than facilitate improved health outcomes. In brief, there is now no easy way to measure how much “health” is conferred by a health system. However, we argue that this goal is achievable. Health-weighted composite quality metrics have the potential to measure “health” by synthesizing individual evidence-based quality metrics into a summary measure, utilizing relative weightings that reflect the relative amount of health benefit conferred by each constituent quality metric. Previously, it has been challenging to create health-weighted composite quality metrics because of methodological and data limitations. However, advances in health information technology and mathematical modeling of disease progression promise to help mitigate these challenges by making patient-level data (eg, from the electronic health record and mobile health (mHealth) more accessible and more actionable for use. Accordingly, it may now be possible to use health information technology to calculate and track a health-weighted composite quality metric for each patient that reflects the health benefit conferred to that patient by the health system. These health-weighted composite quality metrics can be employed for a multitude of important aims that improve health outcomes, including quality evaluation, population health maximization, health disparity attenuation, panel management, resource allocation, and personalization of care. We describe the necessary attributes, the possible uses, and the likely limitations and challenges of health-weighted composite quality metrics using patient-level health data. |
format | Online Article Text |
id | pubmed-4371421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | AcademyHealth |
record_format | MEDLINE/PubMed |
spelling | pubmed-43714212015-04-06 Health-weighted Composite Quality Metrics Offer Promise to Improve Health Outcomes in a Learning Health System Braithwaite, Scott Stine, Nicholas EGEMS (Wash DC) Learning Health System Health system leaders sometimes adopt quality metrics without robust supporting evidence of improvements in quality and/or quantity of life, which may impair rather than facilitate improved health outcomes. In brief, there is now no easy way to measure how much “health” is conferred by a health system. However, we argue that this goal is achievable. Health-weighted composite quality metrics have the potential to measure “health” by synthesizing individual evidence-based quality metrics into a summary measure, utilizing relative weightings that reflect the relative amount of health benefit conferred by each constituent quality metric. Previously, it has been challenging to create health-weighted composite quality metrics because of methodological and data limitations. However, advances in health information technology and mathematical modeling of disease progression promise to help mitigate these challenges by making patient-level data (eg, from the electronic health record and mobile health (mHealth) more accessible and more actionable for use. Accordingly, it may now be possible to use health information technology to calculate and track a health-weighted composite quality metric for each patient that reflects the health benefit conferred to that patient by the health system. These health-weighted composite quality metrics can be employed for a multitude of important aims that improve health outcomes, including quality evaluation, population health maximization, health disparity attenuation, panel management, resource allocation, and personalization of care. We describe the necessary attributes, the possible uses, and the likely limitations and challenges of health-weighted composite quality metrics using patient-level health data. AcademyHealth 2013-10-28 /pmc/articles/PMC4371421/ /pubmed/25848572 http://dx.doi.org/10.13063/2327-9214.1022 Text en All eGEMs publications are licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License http://creativecommons.org/licenses/by-nc-nd/3.0/ |
spellingShingle | Learning Health System Braithwaite, Scott Stine, Nicholas Health-weighted Composite Quality Metrics Offer Promise to Improve Health Outcomes in a Learning Health System |
title | Health-weighted Composite Quality Metrics Offer Promise to Improve Health Outcomes in a Learning Health System |
title_full | Health-weighted Composite Quality Metrics Offer Promise to Improve Health Outcomes in a Learning Health System |
title_fullStr | Health-weighted Composite Quality Metrics Offer Promise to Improve Health Outcomes in a Learning Health System |
title_full_unstemmed | Health-weighted Composite Quality Metrics Offer Promise to Improve Health Outcomes in a Learning Health System |
title_short | Health-weighted Composite Quality Metrics Offer Promise to Improve Health Outcomes in a Learning Health System |
title_sort | health-weighted composite quality metrics offer promise to improve health outcomes in a learning health system |
topic | Learning Health System |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4371421/ https://www.ncbi.nlm.nih.gov/pubmed/25848572 http://dx.doi.org/10.13063/2327-9214.1022 |
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