Interpreting and coding causal relationships for quality and safety using ICD-11
Abstract 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 c...
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02363-5 |
| _version_ | 1797559231042289664 |
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| author | Jean-Marie Januel Danielle A. Southern William A. Ghali |
| author_facet | Jean-Marie Januel Danielle A. Southern William A. Ghali |
| author_sort | Jean-Marie Januel |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-10T17:42:29Z |
| format | Article |
| id | doaj.art-16ecaf06fab6435aa1c55b3523d55058 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-10T17:42:29Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-16ecaf06fab6435aa1c55b3523d550582023-11-20T09:38:07ZengBMCBMC Medical Informatics and Decision Making1472-69472023-11-0121S611010.1186/s12911-023-02363-5Interpreting and coding causal relationships for quality and safety using ICD-11Jean-Marie Januel0Danielle A. Southern1William A. Ghali2Department of Biomedical Informatics, Rouen University HospitalCentre for Health Informatics, Cumming School of Medicine, University of CalgaryCentre for Health Informatics, Cumming School of Medicine, University of CalgaryAbstract 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.https://doi.org/10.1186/s12911-023-02363-5CausationInternational Classification of DiseasesQuality and safetyAdverse eventsICD-11 |
| spellingShingle | Jean-Marie Januel Danielle A. Southern William A. Ghali Interpreting and coding causal relationships for quality and safety using ICD-11 BMC Medical Informatics and Decision Making Causation International Classification of Diseases Quality and safety Adverse events 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 | Causation International Classification of Diseases Quality and safety Adverse events ICD-11 |
| url | https://doi.org/10.1186/s12911-023-02363-5 |
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