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A prototype of knowledge-based patient safety event reporting and learning system
BACKGROUND: Patient falls, the most common safety events resulting in adverse patient outcomes, impose significant costs and have become a great burden to the healthcare community. Current patient fall reporting systems remain in the early stage that is far away from reaching the ultimate goal towar...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284264/ https://www.ncbi.nlm.nih.gov/pubmed/30526567 http://dx.doi.org/10.1186/s12911-018-0688-5 |
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author | Kang, Hong Zhou, Sicheng Yao, Bin Gong, Yang |
author_facet | Kang, Hong Zhou, Sicheng Yao, Bin Gong, Yang |
author_sort | Kang, Hong |
collection | PubMed |
description | BACKGROUND: Patient falls, the most common safety events resulting in adverse patient outcomes, impose significant costs and have become a great burden to the healthcare community. Current patient fall reporting systems remain in the early stage that is far away from reaching the ultimate goal toward a safer healthcare. According to the Kirkpatrick model, the key challenge in reaction, learning, behavior and results is the realization of learning stage due to the lack of knowledge management, sharing and growing mechanism. METHODS: Based on the key contributing factors defined by AHRQ Common Formats 2.0, a hierarchical list of contributing factors for patient falls was established by expert review and discussion. Using the list as an infrastructure, we designed and developed a novel reporting system, where a strategy to identify contributing factors is intended to provide reporters knowledge support, in the form of similar cases and potential solutions. A survey containing two scenarios was conducted to evaluate the learning effect of our system. RESULTS: In both scenarios, potential solutions recommended by the system were annotated with correct contributing factors, and presented only when the corresponding factors were identified from the query report or selected by the user. The five experts show substantial consistency (Fleiss’ kappa > 0.6) and high agreement (ranging between fully agree and mostly agree) in the assessment of the three perspectives of the system, which verifies the effectiveness of the proposed knowledge support toward sharing and learning through the novel reporting system. CONCLUSIONS: This study proposed a profile of contributing factors that could measure the similarity of patient safety events. Based on the profile, a knowledge-based reporting and learning system was developed to bridge the gap between surveillance, reporting, and retrospective analysis in the fall management circle. The system holds promise in improving event reporting toward better and safer healthcare. |
format | Online Article Text |
id | pubmed-6284264 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-62842642018-12-14 A prototype of knowledge-based patient safety event reporting and learning system Kang, Hong Zhou, Sicheng Yao, Bin Gong, Yang BMC Med Inform Decis Mak Research BACKGROUND: Patient falls, the most common safety events resulting in adverse patient outcomes, impose significant costs and have become a great burden to the healthcare community. Current patient fall reporting systems remain in the early stage that is far away from reaching the ultimate goal toward a safer healthcare. According to the Kirkpatrick model, the key challenge in reaction, learning, behavior and results is the realization of learning stage due to the lack of knowledge management, sharing and growing mechanism. METHODS: Based on the key contributing factors defined by AHRQ Common Formats 2.0, a hierarchical list of contributing factors for patient falls was established by expert review and discussion. Using the list as an infrastructure, we designed and developed a novel reporting system, where a strategy to identify contributing factors is intended to provide reporters knowledge support, in the form of similar cases and potential solutions. A survey containing two scenarios was conducted to evaluate the learning effect of our system. RESULTS: In both scenarios, potential solutions recommended by the system were annotated with correct contributing factors, and presented only when the corresponding factors were identified from the query report or selected by the user. The five experts show substantial consistency (Fleiss’ kappa > 0.6) and high agreement (ranging between fully agree and mostly agree) in the assessment of the three perspectives of the system, which verifies the effectiveness of the proposed knowledge support toward sharing and learning through the novel reporting system. CONCLUSIONS: This study proposed a profile of contributing factors that could measure the similarity of patient safety events. Based on the profile, a knowledge-based reporting and learning system was developed to bridge the gap between surveillance, reporting, and retrospective analysis in the fall management circle. The system holds promise in improving event reporting toward better and safer healthcare. BioMed Central 2018-12-07 /pmc/articles/PMC6284264/ /pubmed/30526567 http://dx.doi.org/10.1186/s12911-018-0688-5 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Kang, Hong Zhou, Sicheng Yao, Bin Gong, Yang A prototype of knowledge-based patient safety event reporting and learning system |
title | A prototype of knowledge-based patient safety event reporting and learning system |
title_full | A prototype of knowledge-based patient safety event reporting and learning system |
title_fullStr | A prototype of knowledge-based patient safety event reporting and learning system |
title_full_unstemmed | A prototype of knowledge-based patient safety event reporting and learning system |
title_short | A prototype of knowledge-based patient safety event reporting and learning system |
title_sort | prototype of knowledge-based patient safety event reporting and learning system |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284264/ https://www.ncbi.nlm.nih.gov/pubmed/30526567 http://dx.doi.org/10.1186/s12911-018-0688-5 |
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