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Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.
Abdel-Hafez, Ahmad; Jones, Melanie; Ebrahimabadi, Maziiar; Ryan, Cathi; Graham, Steve; Slee, Nicola; Whitfield, Bernard.
Afiliação
  • Abdel-Hafez A; College of Computing & Information Technology, University of Doha for Science and Technology, Doha, Qatar.
  • Jones M; Clinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.
  • Ebrahimabadi M; Clinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.
  • Ryan C; Clinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.
  • Graham S; Clinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.
  • Slee N; Clinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.
  • Whitfield B; Paediatric Otolaryngology Head and Neck Surgery, Queensland Children's Hospital, Brisbane, QLD, Australia.
Front Digit Health ; 5: 1192975, 2023.
Article em En | MEDLINE | ID: mdl-37964894
ABSTRACT
The clinical prioritisation criteria (CPC) are a clinical decision support tool that ensures patients referred for public specialist outpatient services to Queensland Health are assessed according to their clinical urgency. Medical referrals are manually triaged and prioritised into three categories by the associated health service before appointments are booked. We have developed a method using artificial intelligence to automate the process of categorizing medical referrals based on clinical prioritization criteria (CPC) guidelines. Using machine learning techniques, we have created a tool that can assist clinicians in sorting through the substantial number of referrals they receive each year, leading to more efficient use of clinical specialists' time and improved access to healthcare for patients. Our research included analyzing 17,378 ENT referrals from two hospitals in Queensland between 2019 and 2022. Our results show a level of agreement between referral categories and generated predictions of 53.8%.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article