DECISION SUPPORT FOR CLINICAL LABORATORY TEST REQUISITION: THE UTILITY OF ICD-10 CODING
DOI:
https://doi.org/10.7166/33-1-2556Keywords:
healthcare analytics, data mining, big data, decision support, smart laboratoriesAbstract
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.
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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
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