Journal of Epidemiology
Online ISSN : 1349-9092
Print ISSN : 0917-5040
Original Article
The Proportion of Uncoded Diagnoses in Computerized Health Insurance Claims in Japan in May 2010 According to ICD-10 Disease Categories
Shinichi Tanihara
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2014 Volume 24 Issue 5 Pages 392-396


Background: Uncoded diagnoses in computerized health insurance claims are excluded from statistical summaries of health-related risks and other factors. The effects of these uncoded diagnoses, coded according to ICD-10 disease categories, have not been investigated to date in Japan.
Methods: I obtained all computerized health insurance claims (outpatient medical care, inpatient medical care, and diagnosis procedure-combination per-diem payment system [DPC/PDPS] claims) submitted to the National Health Insurance Organization of Kumamoto Prefecture in May 2010. These were classified according to the disease categories of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). I used accompanying text documentation related to the uncoded diagnoses to classify these diagnoses. Using these classifications, I calculated the proportion of uncoded diagnoses by ICD-10 category.
Results: The number of analyzed diagnoses was 3 804 246, with uncoded diagnoses accounting for 9.6% of the total. The proportion of uncoded diagnoses in claims for outpatient medical care, inpatient medical care, and DPC/PDPS were 9.3%, 10.9%, and 14.2%, respectively. Among the diagnoses, Congenital malformations, deformations, and chromosomal abnormalities had the highest proportion of uncoded diagnoses (19.3%), and Diseases of the respiratory system had the lowest proportion of uncoded diagnoses (4.7%).
Conclusions: The proportion of uncoded diagnoses differed by the type of health insurance claim and disease category. These findings indicate that Japanese health statistics computed using computerized health insurance claims might be biased by the exclusion of uncoded diagnoses.

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© 2014 Shinichi Tanihara. This is an open access article distributed under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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