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Clinically meaningful categorisation of ICD-10-AM (Australian modification)

Authors :
Duke, Graeme J.
Hirth, Steven
Santamaria, John D.
Read, Carla
Hamilton, Adina
Lau, Melisa
Fernando, Tharanga
Li, Zhuoyang
Le, Teresa
Walkley, Kirstie
Source :
Health Information Management Journal. Nov2024, p1.
Publication Year :
2024

Abstract

<bold>Background:</bold> Current methods of categorising the <italic>International Statistical Classification of Diseases and Related Health Problems</italic> (ICD) have limitations when deciphering administrative data and monitoring healthcare outcomes. These include many-to-one relationships, non-linear sequencing, collinearity, and ambiguous miscellaneous (residual) codes. <bold>Objective:</bold> Describe novel methodology for clinically meaningful categorisation of 12th Edition of ICD Version 10 Australian modification (ICD-10-AM). <bold>Setting:</bold> State of Victoria (Australia), population of 6.6 million with over 3 million separations per annum. <bold>Method:</bold> Diagnosis codes from ICD-10-AM were aggregated into Clinical Diagnosis Group (CDG) sets according to clinical features and associated risk of in-hospital death and complications. Residual codes were excluded. Administrative data from July 2020 to June 2023 were interrogated to ascertain frequency of diagnoses captured by CDG sets. <bold>Results:</bold> 12,716 (87.9%) of 14,470 total ICD-10-AM codes were aggregated into 406 CDG sets; mean 32 (range 1–288) codes per set. One thousand seven hundred fifty-three (12.1%) were excluded (not allocated): 775 (5.4%) residual codes; 702 (4.9%) indicating reason for healthcare encounter; and 276 (1.9%) ill-defined clinical symptom codes. Over 36-months, 11.8 million separations were coded with 11,898 (82.2%) unique ICD-10-AM diagnoses, including 10,721 (90.1%) present in a CDG set. Of the 8571 (59.2%) codes associated with death or complications, 7813 (91.2%) were present in a CDG set. <bold>Conclusion:</bold> The CDG list provides a clinically meaningful method of categorisation and interrogating datasets based on ICD-10-AM and complements existing methods. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18333583
Database :
Academic Search Index
Journal :
Health Information Management Journal
Publication Type :
Academic Journal
Accession number :
181017403
Full Text :
https://doi.org/10.1177/18333583241296224