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Implementation of the CHA IPSO Collaborative at a Pediatric Academic Center
INTRODUCTION: Boston Children’s Hospital joined a national quality improvement collaborative to reduce the incidence of severe sepsis (SS) and associated mortality through early identification and treatment. We developed a scalable data infrastructure to retrospectively identify SS and non-severe se...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132768/ http://dx.doi.org/10.1097/pq9.0000000000000073 |
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author | Mathur, Himi Zapata, Lesly Zhan, Xin Kahlon, Prerna S. |
author_facet | Mathur, Himi Zapata, Lesly Zhan, Xin Kahlon, Prerna S. |
author_sort | Mathur, Himi |
collection | PubMed |
description | INTRODUCTION: Boston Children’s Hospital joined a national quality improvement collaborative to reduce the incidence of severe sepsis (SS) and associated mortality through early identification and treatment. We developed a scalable data infrastructure to retrospectively identify SS and non-severe sepsis (NSS) patients from the data warehouse (EDW) to identify opportunities for quality improvement by April 2017. METHODS: Developed a 3-stage scalable process based on techniques involved in retrospective identification of SS patients by using RedCap(TM), as the tool of choice (see Figure 1, Supplemental Digital Content 1, available at http://links.lww.com/PQ9/A22). 1. Retrospective Data Mining: SS and NSS patients are extracted from the EDW by SQL query based on the treatment criteria specified by IPSO (Fig. 1) 2. Data Verification: Data from the EDW is automatically extracted into a patient specific RedCap(TM) screening form for all patients filtered through the query logic into automated and manual fields, for data coordinator and clinical verification 3. Data submission: RedCap(TM) submission form is a completely automated extension of the Redcap(TM) screening form to extract data sepsis quality variables for all patients. An MS Excel version of this form is submitted to IPSO data portal RESULTS: The data mining accuracy improved by 46% and time spent per manual review decreased from 20 minutes to 6 minutes after implementing the 3-stage process. CONCLUSIONS: Continuously optimize data collection through an iterative process by minimizing manual review to improve the identification of pediatric sepsis patients. Lessons learned from our process are definitely transferable and can be customized to other IPSO participants. |
format | Online Article Text |
id | pubmed-6132768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Wolters Kluwer Health |
record_format | MEDLINE/PubMed |
spelling | pubmed-61327682018-10-02 Implementation of the CHA IPSO Collaborative at a Pediatric Academic Center Mathur, Himi Zapata, Lesly Zhan, Xin Kahlon, Prerna S. Pediatr Qual Saf Symposium Proceedings: Improving Pediatric Sepsis Outcomes Colloquium – Dallas TX, December 2017 INTRODUCTION: Boston Children’s Hospital joined a national quality improvement collaborative to reduce the incidence of severe sepsis (SS) and associated mortality through early identification and treatment. We developed a scalable data infrastructure to retrospectively identify SS and non-severe sepsis (NSS) patients from the data warehouse (EDW) to identify opportunities for quality improvement by April 2017. METHODS: Developed a 3-stage scalable process based on techniques involved in retrospective identification of SS patients by using RedCap(TM), as the tool of choice (see Figure 1, Supplemental Digital Content 1, available at http://links.lww.com/PQ9/A22). 1. Retrospective Data Mining: SS and NSS patients are extracted from the EDW by SQL query based on the treatment criteria specified by IPSO (Fig. 1) 2. Data Verification: Data from the EDW is automatically extracted into a patient specific RedCap(TM) screening form for all patients filtered through the query logic into automated and manual fields, for data coordinator and clinical verification 3. Data submission: RedCap(TM) submission form is a completely automated extension of the Redcap(TM) screening form to extract data sepsis quality variables for all patients. An MS Excel version of this form is submitted to IPSO data portal RESULTS: The data mining accuracy improved by 46% and time spent per manual review decreased from 20 minutes to 6 minutes after implementing the 3-stage process. CONCLUSIONS: Continuously optimize data collection through an iterative process by minimizing manual review to improve the identification of pediatric sepsis patients. Lessons learned from our process are definitely transferable and can be customized to other IPSO participants. Wolters Kluwer Health 2018-04-17 /pmc/articles/PMC6132768/ http://dx.doi.org/10.1097/pq9.0000000000000073 Text en Copyright © 2018 the Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Symposium Proceedings: Improving Pediatric Sepsis Outcomes Colloquium – Dallas TX, December 2017 Mathur, Himi Zapata, Lesly Zhan, Xin Kahlon, Prerna S. Implementation of the CHA IPSO Collaborative at a Pediatric Academic Center |
title | Implementation of the CHA IPSO Collaborative at a Pediatric Academic Center |
title_full | Implementation of the CHA IPSO Collaborative at a Pediatric Academic Center |
title_fullStr | Implementation of the CHA IPSO Collaborative at a Pediatric Academic Center |
title_full_unstemmed | Implementation of the CHA IPSO Collaborative at a Pediatric Academic Center |
title_short | Implementation of the CHA IPSO Collaborative at a Pediatric Academic Center |
title_sort | implementation of the cha ipso collaborative at a pediatric academic center |
topic | Symposium Proceedings: Improving Pediatric Sepsis Outcomes Colloquium – Dallas TX, December 2017 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132768/ http://dx.doi.org/10.1097/pq9.0000000000000073 |
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