Kin Wah Fung1, Julia Xu1, Olivier Bodenreider1. 1. Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
Abstract
OBJECTIVE: To study the newly adopted International Classification of Diseases 11th revision (ICD-11) and compare it to the International Classification of Diseases 10th revision (ICD-10) and International Classification of Diseases 10th revision-Clinical Modification (ICD-10-CM). MATERIALS AND METHODS: : Data files and maps were downloaded from the World Health Organization (WHO) website and through the application programming interfaces. A round trip method based on the WHO maps was used to identify equivalent codes between ICD-10 and ICD-11, which were validated by limited manual review. ICD-11 terms were mapped to ICD-10-CM through normalized lexical mapping. ICD-10-CM codes in 6 disease areas were also manually recoded in ICD-11. RESULTS: Excluding the chapters for traditional medicine, functioning assessment, and extension codes for postcoordination, ICD-11 has 14 622 leaf codes (codes that can be used in coding) compared to ICD-10 and ICD-10-CM, which has 10 607 and 71 932 leaf codes, respectively. We identified 4037 pairs of ICD-10 and ICD-11 codes that were equivalent (estimated accuracy of 96%) by our round trip method. Lexical matching between ICD-11 and ICD-10-CM identified 4059 pairs of possibly equivalent codes. Manual recoding showed that 60% of a sample of 388 ICD-10-CM codes could be fully represented in ICD-11 by precoordinated codes or postcoordination. CONCLUSION: In ICD-11, there is a moderate increase in the number of codes over ICD-10. With postcoordination, it is possible to fully represent the meaning of a high proportion of ICD-10-CM codes, especially with the addition of a limited number of extension codes. Published by Oxford University Press on behalf of the American Medical Informatics Association 2020. This work is written by US Government employees and is in the public domain in the US.
OBJECTIVE: To study the newly adopted International Classification of Diseases 11th revision (ICD-11) and compare it to the International Classification of Diseases 10th revision (ICD-10) and International Classification of Diseases 10th revision-Clinical Modification (ICD-10-CM). MATERIALS AND METHODS: : Data files and maps were downloaded from the World Health Organization (WHO) website and through the application programming interfaces. A round trip method based on the WHO maps was used to identify equivalent codes between ICD-10 and ICD-11, which were validated by limited manual review. ICD-11 terms were mapped to ICD-10-CM through normalized lexical mapping. ICD-10-CM codes in 6 disease areas were also manually recoded in ICD-11. RESULTS: Excluding the chapters for traditional medicine, functioning assessment, and extension codes for postcoordination, ICD-11 has 14 622 leaf codes (codes that can be used in coding) compared to ICD-10 and ICD-10-CM, which has 10 607 and 71 932 leaf codes, respectively. We identified 4037 pairs of ICD-10 and ICD-11 codes that were equivalent (estimated accuracy of 96%) by our round trip method. Lexical matching between ICD-11 and ICD-10-CM identified 4059 pairs of possibly equivalent codes. Manual recoding showed that 60% of a sample of 388 ICD-10-CM codes could be fully represented in ICD-11 by precoordinated codes or postcoordination. CONCLUSION: In ICD-11, there is a moderate increase in the number of codes over ICD-10. With postcoordination, it is possible to fully represent the meaning of a high proportion of ICD-10-CM codes, especially with the addition of a limited number of extension codes. Published by Oxford University Press on behalf of the American Medical Informatics Association 2020. This work is written by US Government employees and is in the public domain in the US.
Keywords:
ICD-10; ICD-10-CM; ICD-11; controlled medical vocabularies; medical terminologies
Authors: Luciana Kase Tanno; Robert Chalmers; Ana Luiza Bierrenbach; F Estelle R Simons; Bryan Martin; Nicolas Molinari; Isabella Annesi-Maesano; Margitta Worm; Victoria Cardona; Nikolaos G Papadopoulos; Mario Sanchez-Borges; Lanny J Rosenwasser; Ignacio Ansontegui; Motohiro Ebisawa; Juan Carlos Sisul; Edgardo Jares; Maximiliano Gomez; Ioana Agache; Peter Hellings; Antonella Muraro; Francis Thien; Ruby Pawankar; James L Sublett; Thomas Casale; Pascal Demoly Journal: J Allergy Clin Immunol Date: 2019-06-20 Impact factor: 10.793
Authors: Stefan Schulz; Jean-M Rodrigues; Alan Rector; Kent Spackman; James Campbell; Bedirhan Ustün; Christopher G Chute; Harold Solbrig; Vincenzo Della Mea; Jane Millar; Kristina Brand Persson Journal: Stud Health Technol Inform Date: 2014
Authors: Samuel N Grief; Jesal Patel; Karl M Kochendorfer; Lee A Green; Yves A Lussier; Jianrong Li; Michael Burton; Andrew D Boyd Journal: J Am Board Fam Med Date: 2016 Jan-Feb Impact factor: 2.657
Authors: Andrew D Boyd; Jianrong John Li; Colleen Kenost; Binoy Joese; Young Min Yang; Olympia A Kalagidis; Ilir Zenku; Donald Saner; Neil Bahroos; Yves A Lussier Journal: J Am Med Inform Assoc Date: 2015-02-12 Impact factor: 4.497
Authors: Jean-Marie Rodrigues; David Robinson; Vincenzo Della Mea; James Campbell; Alan Rector; Stefan Schulz; Hazel Brear; Bedirhan Üstün; Kent Spackman; Christopher G Chute; Jane Millar; Harold Solbrig; Kristina Brand Persson Journal: Stud Health Technol Inform Date: 2015
Authors: Kin Wah Fung; Julia Xu; Shannon McConnell-Lamptey; Donna Pickett; Olivier Bodenreider Journal: J Am Med Inform Assoc Date: 2021-10-12 Impact factor: 7.942
Authors: Evelina Tacconelli; Anna Gorska; Elena Carrara; Ruth Joanna Davis; Marc Bonten; Alex W Friedrich; Corinna Glasner; Herman Goossens; Jan Hasenauer; Josep Maria Haro Abad; José L Peñalvo; Albert Sanchez-Niubo; Anastassja Sialm; Gabriella Scipione; Gloria Soriano; Yazdan Yazdanpanah; Ellen Vorstenbosch; Thomas Jaenisch Journal: Lancet Reg Health Eur Date: 2022-08-04