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Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol
BACKGROUND: The prescribing of high-risk medications to older adults remains extremely common and results in potentially avoidable health consequences. Efforts to reduce prescribing have had limited success, in part because they have been sub-optimally timed, poorly designed, or not provided actiona...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7792313/ https://www.ncbi.nlm.nih.gov/pubmed/33413494 http://dx.doi.org/10.1186/s13012-020-01078-9 |
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author | Lauffenburger, Julie C. Isaac, Thomas Trippa, Lorenzo Keller, Punam Robertson, Ted Glynn, Robert J. Sequist, Thomas D. Kim, Dae H. Fontanet, Constance P. Castonguay, Edward W. B. Haff, Nancy Barlev, Renee A. Mahesri, Mufaddal Gopalakrishnan, Chandrashekar Choudhry, Niteesh K. |
author_facet | Lauffenburger, Julie C. Isaac, Thomas Trippa, Lorenzo Keller, Punam Robertson, Ted Glynn, Robert J. Sequist, Thomas D. Kim, Dae H. Fontanet, Constance P. Castonguay, Edward W. B. Haff, Nancy Barlev, Renee A. Mahesri, Mufaddal Gopalakrishnan, Chandrashekar Choudhry, Niteesh K. |
author_sort | Lauffenburger, Julie C. |
collection | PubMed |
description | BACKGROUND: The prescribing of high-risk medications to older adults remains extremely common and results in potentially avoidable health consequences. Efforts to reduce prescribing have had limited success, in part because they have been sub-optimally timed, poorly designed, or not provided actionable information. Electronic health record (EHR)-based tools are commonly used but have had limited application in facilitating deprescribing in older adults. The objective is to determine whether designing EHR tools using behavioral science principles reduces inappropriate prescribing and clinical outcomes in older adults. METHODS: The Novel Uses of Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) project uses a two-stage, 16-arm adaptive randomized pragmatic trial with a “pick-the-winner” design to identify the most effective of many potential EHR tools among primary care providers and their patients ≥ 65 years chronically using benzodiazepines, sedative hypnotic (“Z-drugs”), or anticholinergics in a large integrated delivery system. In stage 1, we randomized providers and their patients to usual care (n = 81 providers) or one of 15 EHR tools (n = 8 providers per arm) designed using behavioral principles including salience, choice architecture, or defaulting. After 6 months of follow-up, we will rank order the arms based upon their impact on the trial’s primary outcome (for both stages): reduction in inappropriate prescribing (via discontinuation or tapering). In stage 2, we will randomize (a) stage 1 usual care providers in a 1:1 ratio to one of the up to 5 most promising stage 1 interventions or continue usual care and (b) stage 1 providers in the unselected arms in a 1:1 ratio to one of the 5 most promising interventions or usual care. Secondary and tertiary outcomes include quantities of medication prescribed and utilized and clinically significant adverse outcomes. DISCUSSION: Stage 1 launched in October 2020. We plan to complete stage 2 follow-up in December 2021. These results will advance understanding about how behavioral science can optimize EHR decision support to improve prescribing and health outcomes. Adaptive trials have rarely been used in implementation science, so these findings also provide insight into how trials in this field could be more efficiently conducted. TRIAL REGISTRATION: Clinicaltrials.gov (NCT04284553, registered: February 26, 2020) SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13012-020-01078-9. |
format | Online Article Text |
id | pubmed-7792313 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77923132021-01-11 Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol Lauffenburger, Julie C. Isaac, Thomas Trippa, Lorenzo Keller, Punam Robertson, Ted Glynn, Robert J. Sequist, Thomas D. Kim, Dae H. Fontanet, Constance P. Castonguay, Edward W. B. Haff, Nancy Barlev, Renee A. Mahesri, Mufaddal Gopalakrishnan, Chandrashekar Choudhry, Niteesh K. Implement Sci Study Protocol BACKGROUND: The prescribing of high-risk medications to older adults remains extremely common and results in potentially avoidable health consequences. Efforts to reduce prescribing have had limited success, in part because they have been sub-optimally timed, poorly designed, or not provided actionable information. Electronic health record (EHR)-based tools are commonly used but have had limited application in facilitating deprescribing in older adults. The objective is to determine whether designing EHR tools using behavioral science principles reduces inappropriate prescribing and clinical outcomes in older adults. METHODS: The Novel Uses of Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) project uses a two-stage, 16-arm adaptive randomized pragmatic trial with a “pick-the-winner” design to identify the most effective of many potential EHR tools among primary care providers and their patients ≥ 65 years chronically using benzodiazepines, sedative hypnotic (“Z-drugs”), or anticholinergics in a large integrated delivery system. In stage 1, we randomized providers and their patients to usual care (n = 81 providers) or one of 15 EHR tools (n = 8 providers per arm) designed using behavioral principles including salience, choice architecture, or defaulting. After 6 months of follow-up, we will rank order the arms based upon their impact on the trial’s primary outcome (for both stages): reduction in inappropriate prescribing (via discontinuation or tapering). In stage 2, we will randomize (a) stage 1 usual care providers in a 1:1 ratio to one of the up to 5 most promising stage 1 interventions or continue usual care and (b) stage 1 providers in the unselected arms in a 1:1 ratio to one of the 5 most promising interventions or usual care. Secondary and tertiary outcomes include quantities of medication prescribed and utilized and clinically significant adverse outcomes. DISCUSSION: Stage 1 launched in October 2020. We plan to complete stage 2 follow-up in December 2021. These results will advance understanding about how behavioral science can optimize EHR decision support to improve prescribing and health outcomes. Adaptive trials have rarely been used in implementation science, so these findings also provide insight into how trials in this field could be more efficiently conducted. TRIAL REGISTRATION: Clinicaltrials.gov (NCT04284553, registered: February 26, 2020) SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13012-020-01078-9. BioMed Central 2021-01-07 /pmc/articles/PMC7792313/ /pubmed/33413494 http://dx.doi.org/10.1186/s13012-020-01078-9 Text en © The Author(s) 2021 Open AccessThis 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/. 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 in a credit line to the data. |
spellingShingle | Study Protocol Lauffenburger, Julie C. Isaac, Thomas Trippa, Lorenzo Keller, Punam Robertson, Ted Glynn, Robert J. Sequist, Thomas D. Kim, Dae H. Fontanet, Constance P. Castonguay, Edward W. B. Haff, Nancy Barlev, Renee A. Mahesri, Mufaddal Gopalakrishnan, Chandrashekar Choudhry, Niteesh K. Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol |
title | Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol |
title_full | Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol |
title_fullStr | Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol |
title_full_unstemmed | Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol |
title_short | Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol |
title_sort | rationale and design of the novel uses of adaptive designs to guide provider engagement in electronic health records (nudge-ehr) pragmatic adaptive randomized trial: a trial protocol |
topic | Study Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7792313/ https://www.ncbi.nlm.nih.gov/pubmed/33413494 http://dx.doi.org/10.1186/s13012-020-01078-9 |
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