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Leveraging electronic medical record functionality to capture adenoma detection rate

Measuring the adenoma detection rate (ADR) is critical to providing quality care, however it is also challenging. We aimed to develop a tool using pre-existing electronic health record (EHR) functions to accurately and easily measure total ADR and to provide real-time feedback for endoscopists. We u...

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Autores principales: Jones, Blake, Scott, Frank I., Espinoza, Jeannine, Laborde, Sydney, Chambers, Micah, Wani, Sachin, Edmundowicz, Steven, Austin, Gregory, Pell, Jonathan, Patel, Swati G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188587/
https://www.ncbi.nlm.nih.gov/pubmed/35690660
http://dx.doi.org/10.1038/s41598-022-13943-2
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author Jones, Blake
Scott, Frank I.
Espinoza, Jeannine
Laborde, Sydney
Chambers, Micah
Wani, Sachin
Edmundowicz, Steven
Austin, Gregory
Pell, Jonathan
Patel, Swati G.
author_facet Jones, Blake
Scott, Frank I.
Espinoza, Jeannine
Laborde, Sydney
Chambers, Micah
Wani, Sachin
Edmundowicz, Steven
Austin, Gregory
Pell, Jonathan
Patel, Swati G.
author_sort Jones, Blake
collection PubMed
description Measuring the adenoma detection rate (ADR) is critical to providing quality care, however it is also challenging. We aimed to develop a tool using pre-existing electronic health record (EHR) functions to accurately and easily measure total ADR and to provide real-time feedback for endoscopists. We utilized the Epic EHR. With the help of an Epic analyst, using existing tools, we developed a method by which endoscopy staff could mark whether an adenoma was detected for a given colonoscopy. Using these responses and all colonoscopies performed by the endoscopist recorded in the EHR, ADR was calculated in a report and displayed to endoscopists within the EHR. One endoscopist piloted the tool, and results of the tool were validated against a manual chart review. Over the pilot period the endoscopist performed 145 colonoscopies, of which 78 had adenomas. The tool correctly identified 76/78 colonoscopies with an adenoma and 67/67 of colonoscopies with no adenomas (97.4% sensitivity, 100% specificity, 98% accuracy). There was no difference in ADR as determined by the tool compared to manual review (53.1% vs. 53.8%, p = 0.912). We successfully developed and pilot tested a tool to measure ADR using existing EHR functionality.
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spelling pubmed-91885872022-06-13 Leveraging electronic medical record functionality to capture adenoma detection rate Jones, Blake Scott, Frank I. Espinoza, Jeannine Laborde, Sydney Chambers, Micah Wani, Sachin Edmundowicz, Steven Austin, Gregory Pell, Jonathan Patel, Swati G. Sci Rep Article Measuring the adenoma detection rate (ADR) is critical to providing quality care, however it is also challenging. We aimed to develop a tool using pre-existing electronic health record (EHR) functions to accurately and easily measure total ADR and to provide real-time feedback for endoscopists. We utilized the Epic EHR. With the help of an Epic analyst, using existing tools, we developed a method by which endoscopy staff could mark whether an adenoma was detected for a given colonoscopy. Using these responses and all colonoscopies performed by the endoscopist recorded in the EHR, ADR was calculated in a report and displayed to endoscopists within the EHR. One endoscopist piloted the tool, and results of the tool were validated against a manual chart review. Over the pilot period the endoscopist performed 145 colonoscopies, of which 78 had adenomas. The tool correctly identified 76/78 colonoscopies with an adenoma and 67/67 of colonoscopies with no adenomas (97.4% sensitivity, 100% specificity, 98% accuracy). There was no difference in ADR as determined by the tool compared to manual review (53.1% vs. 53.8%, p = 0.912). We successfully developed and pilot tested a tool to measure ADR using existing EHR functionality. Nature Publishing Group UK 2022-06-11 /pmc/articles/PMC9188587/ /pubmed/35690660 http://dx.doi.org/10.1038/s41598-022-13943-2 Text en © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Jones, Blake
Scott, Frank I.
Espinoza, Jeannine
Laborde, Sydney
Chambers, Micah
Wani, Sachin
Edmundowicz, Steven
Austin, Gregory
Pell, Jonathan
Patel, Swati G.
Leveraging electronic medical record functionality to capture adenoma detection rate
title Leveraging electronic medical record functionality to capture adenoma detection rate
title_full Leveraging electronic medical record functionality to capture adenoma detection rate
title_fullStr Leveraging electronic medical record functionality to capture adenoma detection rate
title_full_unstemmed Leveraging electronic medical record functionality to capture adenoma detection rate
title_short Leveraging electronic medical record functionality to capture adenoma detection rate
title_sort leveraging electronic medical record functionality to capture adenoma detection rate
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188587/
https://www.ncbi.nlm.nih.gov/pubmed/35690660
http://dx.doi.org/10.1038/s41598-022-13943-2
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