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Making Evidence Actionable: Interactive Dashboards, Bayes, and Health Care Innovation
The results of many large-scale federal or multi-site evaluations are typically compiled into long reports which end up sitting on policymaker’s shelves. Moreover, the information policymakers need from these reports is often buried in the report, may not be remembered, understood, or readily access...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ubiquity Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688542/ https://www.ncbi.nlm.nih.gov/pubmed/31406697 http://dx.doi.org/10.5334/egems.300 |
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author | Bir, Anupa Freeman, Nikki Chew, Robert Smith, Kevin Derzon, James Day, Timothy |
author_facet | Bir, Anupa Freeman, Nikki Chew, Robert Smith, Kevin Derzon, James Day, Timothy |
author_sort | Bir, Anupa |
collection | PubMed |
description | The results of many large-scale federal or multi-site evaluations are typically compiled into long reports which end up sitting on policymaker’s shelves. Moreover, the information policymakers need from these reports is often buried in the report, may not be remembered, understood, or readily accessible to the policymaker when it is needed. This is not a new challenge for evaluators, and advances in statistical methodology, while they have created greater opportunities for insight, may compound the challenge by creating multiple lenses through which evidence can be viewed. The descriptive evidence from traditional frequentist models, while familiar, are frequently misunderstood, while newer Bayesian methods provide evidence which is intuitive, but less familiar. These methods are complementary but presenting both increases the amount of evidence stakeholders and policymakers may find useful. In response to these challenges, we developed an interactive dashboard that synthesizes quantitative and qualitative data and allows users to access the evidence they want, when they want it, allowing each user a customized, and customizable view into the data collected for one large-scale federal evaluation. This offers the opportunity for policymakers to select the specifics that are most relevant to them at any moment, and also apply their own risk tolerance to the probabilities of various outcomes. |
format | Online Article Text |
id | pubmed-6688542 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Ubiquity Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-66885422019-08-12 Making Evidence Actionable: Interactive Dashboards, Bayes, and Health Care Innovation Bir, Anupa Freeman, Nikki Chew, Robert Smith, Kevin Derzon, James Day, Timothy EGEMS (Wash DC) Empirical Research The results of many large-scale federal or multi-site evaluations are typically compiled into long reports which end up sitting on policymaker’s shelves. Moreover, the information policymakers need from these reports is often buried in the report, may not be remembered, understood, or readily accessible to the policymaker when it is needed. This is not a new challenge for evaluators, and advances in statistical methodology, while they have created greater opportunities for insight, may compound the challenge by creating multiple lenses through which evidence can be viewed. The descriptive evidence from traditional frequentist models, while familiar, are frequently misunderstood, while newer Bayesian methods provide evidence which is intuitive, but less familiar. These methods are complementary but presenting both increases the amount of evidence stakeholders and policymakers may find useful. In response to these challenges, we developed an interactive dashboard that synthesizes quantitative and qualitative data and allows users to access the evidence they want, when they want it, allowing each user a customized, and customizable view into the data collected for one large-scale federal evaluation. This offers the opportunity for policymakers to select the specifics that are most relevant to them at any moment, and also apply their own risk tolerance to the probabilities of various outcomes. Ubiquity Press 2019-08-05 /pmc/articles/PMC6688542/ /pubmed/31406697 http://dx.doi.org/10.5334/egems.300 Text en Copyright: © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Empirical Research Bir, Anupa Freeman, Nikki Chew, Robert Smith, Kevin Derzon, James Day, Timothy Making Evidence Actionable: Interactive Dashboards, Bayes, and Health Care Innovation |
title | Making Evidence Actionable: Interactive Dashboards, Bayes, and Health Care Innovation |
title_full | Making Evidence Actionable: Interactive Dashboards, Bayes, and Health Care Innovation |
title_fullStr | Making Evidence Actionable: Interactive Dashboards, Bayes, and Health Care Innovation |
title_full_unstemmed | Making Evidence Actionable: Interactive Dashboards, Bayes, and Health Care Innovation |
title_short | Making Evidence Actionable: Interactive Dashboards, Bayes, and Health Care Innovation |
title_sort | making evidence actionable: interactive dashboards, bayes, and health care innovation |
topic | Empirical Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688542/ https://www.ncbi.nlm.nih.gov/pubmed/31406697 http://dx.doi.org/10.5334/egems.300 |
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