Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria.

artificial intelligence clinical decision support system electronic health records machine learning medical referral natural language processing text mining

Journal

Frontiers in digital health
ISSN: 2673-253X
Titre abrégé: Front Digit Health
Pays: Switzerland
ID NLM: 101771889

Informations de publication

Date de publication:
2023
Historique:
received: 24 03 2023
accepted: 03 10 2023
medline: 15 11 2023
pubmed: 15 11 2023
entrez: 15 11 2023
Statut: epublish

Résumé

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%.

Identifiants

pubmed: 37964894
doi: 10.3389/fdgth.2023.1192975
pmc: PMC10642163
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1192975

Informations de copyright

© 2023 Abdel-Hafez, Jones, Ebrahimabadi, Ryan, Graham, Slee and Whitfield.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Références

Aust Health Rev. 2021 Jun 24;:
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Frontline Gastroenterol. 2019 Jul;10(3):229-235
pubmed: 31281623
IEEE Trans Neural Netw. 1990;1(2):179-91
pubmed: 18282835
Appl Clin Inform. 2018 Jan;9(1):232-237
pubmed: 29590681
Int J Environ Res Public Health. 2022 Jun 16;19(12):
pubmed: 35742633

Auteurs

Ahmad Abdel-Hafez (A)

College of Computing & Information Technology, University of Doha for Science and Technology, Doha, Qatar.
Clinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.

Melanie Jones (M)

Clinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.

Maziiar Ebrahimabadi (M)

Clinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.

Cathi Ryan (C)

Clinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.

Steve Graham (S)

Clinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.

Nicola Slee (N)

Paediatric Otolaryngology Head and Neck Surgery, Queensland Children's Hospital, Brisbane, QLD, Australia.
Medical School, University of Queensland, Brisbane, QLD, Australia.

Bernard Whitfield (B)

Department of Otolaryngology Head and Neck Surgery, Logan Hospital, Meadowbrook, QLD, Australia.
School of Medicine, Griffith University, Southport, QLD, Australia.

Classifications MeSH