Citation

Boyd AD, Li J, Kenost C, Joese B, Min Yang Y, Kalagidis OA, Zenku I, Saner D, Bahroos N, Lussier YA. J. Am. Med. Inform. Assoc. 2015; 22(3): 730-737.

Affiliation

University of Illinois Hospital and Health Science System, Chicago, IL, USA Department of Medicine, University of Arizona, Tucson, AZ, USA The University of Arizona Health Sciences Center, Tucson, AZ, USA Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA Biomedical Informatics Service Group, Arizona Health Science Center, University of Arizona, Tucson, AZ, USA Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, IL, USA Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Cancer Center, Tucson, AZ, USA The work was completed in part at The University of Illinois Lussier.y@gmail.com.

Copyright

(Copyright © 2015, American Medical Informatics Association, Publisher Elsevier Publishing)

DOI

10.1093/jamia/ocu003

PMID

25681260

Abstract

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.

Language: en