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2173. Surgical Site Infection Determination in Epic(®) ICON: A Utilization Model
BACKGROUND: Prior to 2016, our hospital used microbiology results alone to investigate surgical site infections (SSI). Previous studies show that this practice can miss as many as half of clinically significant infections. To improve accuracy for fiscal year 2016 SSI surveillance was done by manual...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6253737/ http://dx.doi.org/10.1093/ofid/ofy210.1829 |
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author | Totten, Sarah Elizabeth Hansen, Shane Barron, Michelle Pisney, Larissa |
author_facet | Totten, Sarah Elizabeth Hansen, Shane Barron, Michelle Pisney, Larissa |
author_sort | Totten, Sarah Elizabeth |
collection | PubMed |
description | BACKGROUND: Prior to 2016, our hospital used microbiology results alone to investigate surgical site infections (SSI). Previous studies show that this practice can miss as many as half of clinically significant infections. To improve accuracy for fiscal year 2016 SSI surveillance was done by manual chart review of 100% of the surgeries we report to NHSN. While more accurate, this process was time and labor intensive. In May 2016, we began using Epic(®) ICON as our data mining software. ICON can abstract (create denominator data), determine SSI status (create numerator data) and upload to NSHN. Data indicates that partially automated SSI surveillance reduce manual chart review but our team found that many charts were being reviewed unnecessarily.We developed a computerized algorithm within ICON that would that would capture SSIs but limit the number of charts to be reviewed. METHODS: Algorithm variables within Epic(®) ICON were modified to limit data collection to the following parameters: readmission, chief complaint, surgical log, diagnosis, antibiotic administration post 48 hours, and specific microbiology results. We excluded 31 keywords that were part of the Epic(®) ICON foundation system from our algorithm. For example, we removed the keyword “infection” which flagged whenever “no infection” was charted. The chief complaints grouper was most important as it allowed only meaningful complaints to be considered. Microbiology results were also limited to only include Aerobic, Anaerobic, Fungus, AFB, and wound cultures. To validate the algorithm, it was run retrospectively for fiscal year 2016. RESULTS: There was 100% concordance of results comparing SSIs identified using chart review to the use of our computerized algorithm and Table 1 shows the average number of charts requiring review pre and post implementation. CONCLUSION: Careful modification of the ICON foundations system resulted in a 55% decrease overall in the need for chart review without affecting accuracy of reporting. DISCLOSURES: All authors: No reported disclosures. |
format | Online Article Text |
id | pubmed-6253737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62537372018-11-28 2173. Surgical Site Infection Determination in Epic(®) ICON: A Utilization Model Totten, Sarah Elizabeth Hansen, Shane Barron, Michelle Pisney, Larissa Open Forum Infect Dis Abstracts BACKGROUND: Prior to 2016, our hospital used microbiology results alone to investigate surgical site infections (SSI). Previous studies show that this practice can miss as many as half of clinically significant infections. To improve accuracy for fiscal year 2016 SSI surveillance was done by manual chart review of 100% of the surgeries we report to NHSN. While more accurate, this process was time and labor intensive. In May 2016, we began using Epic(®) ICON as our data mining software. ICON can abstract (create denominator data), determine SSI status (create numerator data) and upload to NSHN. Data indicates that partially automated SSI surveillance reduce manual chart review but our team found that many charts were being reviewed unnecessarily.We developed a computerized algorithm within ICON that would that would capture SSIs but limit the number of charts to be reviewed. METHODS: Algorithm variables within Epic(®) ICON were modified to limit data collection to the following parameters: readmission, chief complaint, surgical log, diagnosis, antibiotic administration post 48 hours, and specific microbiology results. We excluded 31 keywords that were part of the Epic(®) ICON foundation system from our algorithm. For example, we removed the keyword “infection” which flagged whenever “no infection” was charted. The chief complaints grouper was most important as it allowed only meaningful complaints to be considered. Microbiology results were also limited to only include Aerobic, Anaerobic, Fungus, AFB, and wound cultures. To validate the algorithm, it was run retrospectively for fiscal year 2016. RESULTS: There was 100% concordance of results comparing SSIs identified using chart review to the use of our computerized algorithm and Table 1 shows the average number of charts requiring review pre and post implementation. CONCLUSION: Careful modification of the ICON foundations system resulted in a 55% decrease overall in the need for chart review without affecting accuracy of reporting. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2018-11-26 /pmc/articles/PMC6253737/ http://dx.doi.org/10.1093/ofid/ofy210.1829 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Abstracts Totten, Sarah Elizabeth Hansen, Shane Barron, Michelle Pisney, Larissa 2173. Surgical Site Infection Determination in Epic(®) ICON: A Utilization Model |
title | 2173. Surgical Site Infection Determination in Epic(®) ICON: A Utilization Model |
title_full | 2173. Surgical Site Infection Determination in Epic(®) ICON: A Utilization Model |
title_fullStr | 2173. Surgical Site Infection Determination in Epic(®) ICON: A Utilization Model |
title_full_unstemmed | 2173. Surgical Site Infection Determination in Epic(®) ICON: A Utilization Model |
title_short | 2173. Surgical Site Infection Determination in Epic(®) ICON: A Utilization Model |
title_sort | 2173. surgical site infection determination in epic(®) icon: a utilization model |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6253737/ http://dx.doi.org/10.1093/ofid/ofy210.1829 |
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