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Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM
In the United States, International Classification of Disease Clinical Modification (ICD-9-CM, the ninth revision) diagnosis codes are commonly used to identify patient cohorts and to conduct financial analyses related to disease. In October 2015, the healthcare system of the United States will tran...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457110/ https://www.ncbi.nlm.nih.gov/pubmed/25681260 http://dx.doi.org/10.1093/jamia/ocu003 |
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author | Boyd, Andrew D ‘John’ Li, Jianrong Kenost, Colleen Joese, Binoy Min Yang, Young Kalagidis, Olympia A Zenku, Ilir Saner, Donald Bahroos, Neil Lussier, Yves A |
author_facet | Boyd, Andrew D ‘John’ Li, Jianrong Kenost, Colleen Joese, Binoy Min Yang, Young Kalagidis, Olympia A Zenku, Ilir Saner, Donald Bahroos, Neil Lussier, Yves A |
author_sort | Boyd, Andrew D |
collection | PubMed |
description | In the United States, International Classification of Disease Clinical Modification (ICD-9-CM, the ninth revision) diagnosis codes are commonly used to identify patient cohorts and to conduct financial analyses related to disease. In October 2015, the healthcare system of the United States will transition to ICD-10-CM (the tenth revision) diagnosis codes. One challenge posed to clinical researchers and other analysts is conducting diagnosis-related queries across datasets containing both coding schemes. Further, healthcare administrators will manage growth, trends, and strategic planning with these dually-coded datasets. The majority of the ICD-9-CM to ICD-10-CM translations are complex and nonreciprocal, creating convoluted representations and meanings. Similarly, mapping back from ICD-10-CM to ICD-9-CM is equally complex, yet different from mapping forward, as relationships are likewise nonreciprocal. Indeed, 10 of the 21 top clinical categories are complex as 78% of their diagnosis codes are labeled as “convoluted” by our analyses. Analysis and research related to external causes of morbidity, injury, and poisoning will face the greatest challenges due to 41 745 (90%) convolutions and a decrease in the number of codes. We created a web portal tool and translation tables to list all ICD-9-CM diagnosis codes related to the specific input of ICD-10-CM diagnosis codes and their level of complexity: “identity” (reciprocal), “class-to-subclass,” “subclass-to-class,” “convoluted,” or “no mapping.” These tools provide guidance on ambiguous and complex translations to reveal where reports or analyses may be challenging to impossible. Web portal: http://www.lussierlab.org/transition-to-ICD9CM/ Tables annotated with levels of translation complexity: http://www.lussierlab.org/publications/ICD10to9 |
format | Online Article Text |
id | pubmed-4457110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-44571102016-05-01 Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM Boyd, Andrew D ‘John’ Li, Jianrong Kenost, Colleen Joese, Binoy Min Yang, Young Kalagidis, Olympia A Zenku, Ilir Saner, Donald Bahroos, Neil Lussier, Yves A J Am Med Inform Assoc Brief Communication In the United States, International Classification of Disease Clinical Modification (ICD-9-CM, the ninth revision) diagnosis codes are commonly used to identify patient cohorts and to conduct financial analyses related to disease. In October 2015, the healthcare system of the United States will transition to ICD-10-CM (the tenth revision) diagnosis codes. One challenge posed to clinical researchers and other analysts is conducting diagnosis-related queries across datasets containing both coding schemes. Further, healthcare administrators will manage growth, trends, and strategic planning with these dually-coded datasets. The majority of the ICD-9-CM to ICD-10-CM translations are complex and nonreciprocal, creating convoluted representations and meanings. Similarly, mapping back from ICD-10-CM to ICD-9-CM is equally complex, yet different from mapping forward, as relationships are likewise nonreciprocal. Indeed, 10 of the 21 top clinical categories are complex as 78% of their diagnosis codes are labeled as “convoluted” by our analyses. Analysis and research related to external causes of morbidity, injury, and poisoning will face the greatest challenges due to 41 745 (90%) convolutions and a decrease in the number of codes. We created a web portal tool and translation tables to list all ICD-9-CM diagnosis codes related to the specific input of ICD-10-CM diagnosis codes and their level of complexity: “identity” (reciprocal), “class-to-subclass,” “subclass-to-class,” “convoluted,” or “no mapping.” These tools provide guidance on ambiguous and complex translations to reveal where reports or analyses may be challenging to impossible. Web portal: http://www.lussierlab.org/transition-to-ICD9CM/ Tables annotated with levels of translation complexity: http://www.lussierlab.org/publications/ICD10to9 Oxford University Press 2015-05 2015-02-12 /pmc/articles/PMC4457110/ /pubmed/25681260 http://dx.doi.org/10.1093/jamia/ocu003 Text en © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Brief Communication Boyd, Andrew D ‘John’ Li, Jianrong Kenost, Colleen Joese, Binoy Min Yang, Young Kalagidis, Olympia A Zenku, Ilir Saner, Donald Bahroos, Neil Lussier, Yves A Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM |
title | Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM |
title_full | Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM |
title_fullStr | Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM |
title_full_unstemmed | Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM |
title_short | Metrics and tools for consistent cohort discovery and financial analyses post-transition to ICD-10-CM |
title_sort | metrics and tools for consistent cohort discovery and financial analyses post-transition to icd-10-cm |
topic | Brief Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457110/ https://www.ncbi.nlm.nih.gov/pubmed/25681260 http://dx.doi.org/10.1093/jamia/ocu003 |
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