Evidence map›Paper›PMID 37964894›Full record

ArticleFrontiers in digital health2023

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

Ahmad Abdel-Hafez, Melanie Jones, Maziiar Ebrahimabadi, Cathi Ryan, Steve Graham, Nicola Slee, Bernard Whitfield

Abstract read
In one paragraph

Article in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ahmad Abdel-HafezCollege of Computing & Information Technology, University of Doha for Science and Technology, Doha, Qatar.
Melanie JonesClinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.
Maziiar EbrahimabadiClinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.
Cathi RyanClinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.
Steve GrahamClinical and Business Intelligence (CBI), eHealth, Queensland Health, Brisbane, QLD, Australia.
Nicola SleePaediatric Otolaryngology Head and Neck Surgery, Queensland Children's Hospital, Brisbane, QLD, Australia.
Bernard WhitfieldDepartment of Otolaryngology Head and Neck Surgery, Logan Hospital, Meadowbrook, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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%.

Indexed as

artificial intelligenceclinical decision support systemelectronic health recordsmachine learningmedical referralnatural language processingtext mining

Identifiers

PMID37964894
PMCPMC10642163

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.