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

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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.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. Keywords healthcare analytics; data mining; big data; decision support; smart laboratories

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