DECISION SUPPORT FOR CLINICAL LABORATORY TEST REQUISITION: THE UTILITY OF ICD-10 CODING

Authors

DOI:

https://doi.org/10.7166/33-1-2556

Keywords:

healthcare analytics, data mining, big data, decision support, smart laboratories

Abstract

This study examined the possibility of a strong relationship between ICD-10 codes and the panel of clinical laboratory tests requested. Decision-tree learning principles were used to determine whether requisition event attributes had a useful relationship with laboratory tests. A recommender system was designed and tested using ICD-10 codes as a core predictor. The results showed an average requisition accuracy upwards of 74 per cent. If such a system were to be deployed, health professionals would be able to draw from a vast and accessible pool of knowledge when selecting clinical laboratory tests, improving the effectiveness of clinical laboratory operations.

Author Biographies

Fergus Kenyon Hathorn, Stellenbosch University

BEng Industrial Engineering

Imke H De Kock, Stellenbosch University

Lecturer in the Department of Industrial Engineering

 

Elizabeth Wasserman, Stellenbosch University Pathcare Laboratories

Extraordinary Professor, Division of Medical Microbiology

Pathologist, Clinical Microbiology

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Published

2022-05-06

How to Cite

Hathorn, F. K., De Kock, I. H., & Wasserman, E. (2022). DECISION SUPPORT FOR CLINICAL LABORATORY TEST REQUISITION: THE UTILITY OF ICD-10 CODING. The South African Journal of Industrial Engineering, 33(1), 16–24. https://doi.org/10.7166/33-1-2556

Issue

Vol. 33 No. 1 (2022)

Section

General Articles

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