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Design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors
Delivering clinical decision support (CDS) at the point of care has long been considered a major advantage of computerized physician order entry (CPOE). Despite the widespread implementation of CPOE, medication ordering errors and associated adverse events still occur at an unacceptable level. Previ...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8242134/ https://www.ncbi.nlm.nih.gov/pubmed/34222830 http://dx.doi.org/10.1093/jamiaopen/ooab039 |
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author | Hirsch, Jamie S Brar, Rajdeep Forrer, Christopher Sung, Christine Roycroft, Richard Seelamneni, Pradeep Dabir, Hemala Naseer, Ambareen Gautam-Goyal, Pranisha Bock, Kevin R Oppenheim, Michael I |
author_facet | Hirsch, Jamie S Brar, Rajdeep Forrer, Christopher Sung, Christine Roycroft, Richard Seelamneni, Pradeep Dabir, Hemala Naseer, Ambareen Gautam-Goyal, Pranisha Bock, Kevin R Oppenheim, Michael I |
author_sort | Hirsch, Jamie S |
collection | PubMed |
description | Delivering clinical decision support (CDS) at the point of care has long been considered a major advantage of computerized physician order entry (CPOE). Despite the widespread implementation of CPOE, medication ordering errors and associated adverse events still occur at an unacceptable level. Previous attempts at indication- and kidney function-based dosing have mostly employed intrusive CDS, including interruptive alerts with poor usability. This descriptive work describes the design, development, and deployment of the Adult Dosing Methodology (ADM) module, a novel CDS tool that provides indication- and kidney-based dosing at the time of order entry. Inclusion of several antimicrobials in the initial set of medications allowed for the additional goal of optimizing therapy duration for appropriate antimicrobial stewardship. The CDS aims to decrease order entry errors and burden on providers by offering automatic dose and frequency recommendations, integration within the native electronic health record, and reasonable knowledge maintenance requirements. Following implementation, early utilization demonstrated high acceptance of automated recommendations, with up to 96% of provided automated recommendations accepted by users. |
format | Online Article Text |
id | pubmed-8242134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-82421342021-07-01 Design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors Hirsch, Jamie S Brar, Rajdeep Forrer, Christopher Sung, Christine Roycroft, Richard Seelamneni, Pradeep Dabir, Hemala Naseer, Ambareen Gautam-Goyal, Pranisha Bock, Kevin R Oppenheim, Michael I JAMIA Open Brief Communications Delivering clinical decision support (CDS) at the point of care has long been considered a major advantage of computerized physician order entry (CPOE). Despite the widespread implementation of CPOE, medication ordering errors and associated adverse events still occur at an unacceptable level. Previous attempts at indication- and kidney function-based dosing have mostly employed intrusive CDS, including interruptive alerts with poor usability. This descriptive work describes the design, development, and deployment of the Adult Dosing Methodology (ADM) module, a novel CDS tool that provides indication- and kidney-based dosing at the time of order entry. Inclusion of several antimicrobials in the initial set of medications allowed for the additional goal of optimizing therapy duration for appropriate antimicrobial stewardship. The CDS aims to decrease order entry errors and burden on providers by offering automatic dose and frequency recommendations, integration within the native electronic health record, and reasonable knowledge maintenance requirements. Following implementation, early utilization demonstrated high acceptance of automated recommendations, with up to 96% of provided automated recommendations accepted by users. Oxford University Press 2021-06-30 /pmc/articles/PMC8242134/ /pubmed/34222830 http://dx.doi.org/10.1093/jamiaopen/ooab039 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Brief Communications Hirsch, Jamie S Brar, Rajdeep Forrer, Christopher Sung, Christine Roycroft, Richard Seelamneni, Pradeep Dabir, Hemala Naseer, Ambareen Gautam-Goyal, Pranisha Bock, Kevin R Oppenheim, Michael I Design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors |
title | Design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors |
title_full | Design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors |
title_fullStr | Design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors |
title_full_unstemmed | Design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors |
title_short | Design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors |
title_sort | design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors |
topic | Brief Communications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8242134/ https://www.ncbi.nlm.nih.gov/pubmed/34222830 http://dx.doi.org/10.1093/jamiaopen/ooab039 |
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