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Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification

Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to...

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Autores principales: Kricke, Gayle Shier, Carson, Matthew B, Lee, Young Ji, Benacka, Corrine, Mutharasan, R. Kannan, Ahmad, Faraz S, Kansal, Preeti, Yancy, Clyde W, Anderson, Allen S, Soulakis, Nicholas D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391722/
https://www.ncbi.nlm.nih.gov/pubmed/27589944
http://dx.doi.org/10.1093/jamia/ocw083
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author Kricke, Gayle Shier
Carson, Matthew B
Lee, Young Ji
Benacka, Corrine
Mutharasan, R. Kannan
Ahmad, Faraz S
Kansal, Preeti
Yancy, Clyde W
Anderson, Allen S
Soulakis, Nicholas D
author_facet Kricke, Gayle Shier
Carson, Matthew B
Lee, Young Ji
Benacka, Corrine
Mutharasan, R. Kannan
Ahmad, Faraz S
Kansal, Preeti
Yancy, Clyde W
Anderson, Allen S
Soulakis, Nicholas D
author_sort Kricke, Gayle Shier
collection PubMed
description Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning. Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation. Results: EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation. Conclusion: Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.
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spelling pubmed-53917222017-04-21 Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification Kricke, Gayle Shier Carson, Matthew B Lee, Young Ji Benacka, Corrine Mutharasan, R. Kannan Ahmad, Faraz S Kansal, Preeti Yancy, Clyde W Anderson, Allen S Soulakis, Nicholas D J Am Med Inform Assoc Research and Applications Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning. Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation. Results: EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation. Conclusion: Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps. Oxford University Press 2017-03 2016-09-01 /pmc/articles/PMC5391722/ /pubmed/27589944 http://dx.doi.org/10.1093/jamia/ocw083 Text en © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. http://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/), 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 Research and Applications
Kricke, Gayle Shier
Carson, Matthew B
Lee, Young Ji
Benacka, Corrine
Mutharasan, R. Kannan
Ahmad, Faraz S
Kansal, Preeti
Yancy, Clyde W
Anderson, Allen S
Soulakis, Nicholas D
Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification
title Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification
title_full Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification
title_fullStr Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification
title_full_unstemmed Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification
title_short Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification
title_sort leveraging electronic health record documentation for failure mode and effects analysis team identification
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391722/
https://www.ncbi.nlm.nih.gov/pubmed/27589944
http://dx.doi.org/10.1093/jamia/ocw083
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