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

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 F1 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.

Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria

“…Hence, we anticipate the performance of our method to improve over time as the referral's quality improve. A previous study in this area focused on predicting categories using similarity functions, without predicting CPCs ( 7 ).…”

“…In a related study ( 7 ), the authors introduced a machine learning methodology for medical referral triage using the clinical prioritization criteria (CPC). They collected 3,000 Otorhinolaryngology referrals and used natural language processing (NLP) and cloud services to systematically process and analyse these referrals.…”

Evaluation and Enhancement of Triaging Services Through Quality Improvement Tools at Primary Health Care Level: A Clinical Audit Study

“…Ongoing triage refresher training should be done on a regular basis, which has proven to improve overall triage practice. 12 Enhancement of collaboration between triage physicians and nurses should be promoted for ongoing education and rectification. Use of technology like machine learning techniques will help in improving clinical prioritization to deliver better triaging in accordance with the Clinical Prioritization Criteria.…”

“…Use of technology like machine learning techniques will help in improving clinical prioritization to deliver better triaging in accordance with the Clinical Prioritization Criteria. 12 Mandate all physicians to use the Clinical Information System option to make changes or modifications in the nurse’s triage categorization in case of the wrong categorization. Optimize documentation of patients under the emergency triage category at 3 points of contacts or encounters: with the greeter nurse, the triage nurse, and the physicians, by providing a practical solution to the triage team, for instance, a tablet with a bar code system for noting the real time of the patient journey during the triage process.…”

Human-artificial intelligence interaction in gastrointestinal endoscopy

“…There is a growing list of current and proposed applications for AI in medicine, including direct patient interaction with AI chatbots to answer patient queries, analysis of a large amount of disparate data to predict disease diagnosis and course, and interpretation of images from radiological investigations[ 2 - 4 ]. In gastroenterology, potential clinical applications span from use of domain-specific large-language models (LLMs) in the triage of specialist referrals to prediction of early-stage pancreatic cancer before it becomes overtly visible on imaging[ 5 , 6 ].…”