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Interpreting and coding causal relationships for quality and safety using ICD-11
Many circumstances necessitate judgments regarding causation in health information systems, but these can be tricky in medicine and epidemiology. In this article, we reflect on what the ICD-11 Reference Guide provides on coding for causation and judging when relationships between clinical concepts a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10655490/ https://www.ncbi.nlm.nih.gov/pubmed/37974148 http://dx.doi.org/10.1186/s12911-023-02363-5 |
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author | Januel, Jean-Marie Southern, Danielle A. Ghali, William A. |
author_facet | Januel, Jean-Marie Southern, Danielle A. Ghali, William A. |
author_sort | Januel, Jean-Marie |
collection | PubMed |
description | Many circumstances necessitate judgments regarding causation in health information systems, but these can be tricky in medicine and epidemiology. In this article, we reflect on what the ICD-11 Reference Guide provides on coding for causation and judging when relationships between clinical concepts are causal. Based on the use of different types of codes and the development of a new mechanism for coding potential causal relationships, the ICD-11 provides an in-depth transformation of coding expectations as compared to ICD-10. An essential part of the causal relationship interpretation relies on the presence of “connecting terms,” key elements in assessing the level of certainty regarding a potential relationship and how to proceed in coding a causal relationship using the new ICD-11 coding convention of postcoordination (i.e., clustering of codes). In addition, determining causation involves using documentation from healthcare providers, which is the foundation for coding health information. The coding guidelines and examples (taken from the quality and patient safety domain) presented in this article underline how new ICD-11 features and coding rules will enhance future health information systems and healthcare. |
format | Online Article Text |
id | pubmed-10655490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106554902023-11-16 Interpreting and coding causal relationships for quality and safety using ICD-11 Januel, Jean-Marie Southern, Danielle A. Ghali, William A. BMC Med Inform Decis Mak Review Many circumstances necessitate judgments regarding causation in health information systems, but these can be tricky in medicine and epidemiology. In this article, we reflect on what the ICD-11 Reference Guide provides on coding for causation and judging when relationships between clinical concepts are causal. Based on the use of different types of codes and the development of a new mechanism for coding potential causal relationships, the ICD-11 provides an in-depth transformation of coding expectations as compared to ICD-10. An essential part of the causal relationship interpretation relies on the presence of “connecting terms,” key elements in assessing the level of certainty regarding a potential relationship and how to proceed in coding a causal relationship using the new ICD-11 coding convention of postcoordination (i.e., clustering of codes). In addition, determining causation involves using documentation from healthcare providers, which is the foundation for coding health information. The coding guidelines and examples (taken from the quality and patient safety domain) presented in this article underline how new ICD-11 features and coding rules will enhance future health information systems and healthcare. BioMed Central 2023-11-16 /pmc/articles/PMC10655490/ /pubmed/37974148 http://dx.doi.org/10.1186/s12911-023-02363-5 Text en © The Author(s) 2023 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Januel, Jean-Marie Southern, Danielle A. Ghali, William A. Interpreting and coding causal relationships for quality and safety using ICD-11 |
title | Interpreting and coding causal relationships for quality and safety using ICD-11 |
title_full | Interpreting and coding causal relationships for quality and safety using ICD-11 |
title_fullStr | Interpreting and coding causal relationships for quality and safety using ICD-11 |
title_full_unstemmed | Interpreting and coding causal relationships for quality and safety using ICD-11 |
title_short | Interpreting and coding causal relationships for quality and safety using ICD-11 |
title_sort | interpreting and coding causal relationships for quality and safety using icd-11 |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10655490/ https://www.ncbi.nlm.nih.gov/pubmed/37974148 http://dx.doi.org/10.1186/s12911-023-02363-5 |
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