Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques

Chee Keong Wee, Xujuan Zhou, Ruiliang Sun, Raj Gururajan, Xiaohui Tao, Yuefeng Li and Nathan Wee
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Chee Keong Wee: School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Xujuan Zhou: School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Ruiliang Sun: Digital Application Services, eHealth, Brisbane, QLD 4000, Australia
Raj Gururajan: School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Xiaohui Tao: School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Yuefeng Li: School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
Nathan Wee: Faculty of Science, University of Queensland, Brisbane, QLD 4072, Australia

IJERPH, 2022, vol. 19, issue 12, 1-13

Abstract: Triaging of medical referrals can be completed using various machine learning techniques, but trained models with historical datasets may not be relevant as the clinical criteria for triaging are regularly updated and changed. This paper proposes the use of machine learning techniques coupled with the clinical prioritisation criteria (CPC) of Queensland (QLD), Australia, to deliver better triaging for referrals in accordance with the CPC’s updates. The unique feature of the proposed model is its non-reliance on the past datasets for model training. Medical Natural Language Processing (NLP) was applied in the proposed approach to process the medical referrals, which are unstructured free text. The proposed multiclass classification approach achieved a Micro F 1 score = 0.98. The proposed approach can help in the processing of two million referrals that the QLD health service receives annually; therefore, they can deliver better and more efficient health services.

Keywords: medical NLP; triaging; healthcare AI; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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