Observational studyEmergency medicine journal : EMJ2026
Concordance between an artificial intelligence self-triage programme and physical triage.
Observational study in Emergency medicine journal : EMJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe evaluated the concordance between the Dutch National Triage Standard (NTS) triage classification and the triage of an artificial intelligence (AI) self-triage programme.
methodsThis observational comparative study was performed in the emergency departments (EDs) of two OLVG hospital locations in Amsterdam in October and November 2023. Adult patients who entered the ED without ambulance transport and were triaged using NTS (usual care), were asked to complete the digital AI triage programme. Cohen's kappa (ĸ), per cent agreement and the distribution across urgency categories were used to evaluate the concordance between the triage methods.
resultsOf the 264 patients approached, 203 consented to participation and were included in analysis and follow-up. Agreement between the triage methods was none to slight, ĸ=0.092 (95% CI -0.196 to 0.380). The AI triage programme overtriaged in 12.8% of cases and undertriaged in 5.4% compared with the NTS. However, the AI triage programme classified more patients with serious clinical sequelae into S1 (ie, emergency care with ambulance) and S2 (ie, emergency care) compared with NTS U1 and U2. The AI triage programme predicted the final diagnosis at ED discharge in 27.1% of the cases. There were significantly more clinical sequelae (eg, death, ward admission or follow-up consultation) for general practitioner referred patients compared with self-referrals.
conclusionThe AI triage appeared more distinctive in urgency classification than classic triage when considering clinical sequelae for the patient. However, refinements and better validation of AI triage programmes are needed before these can be implemented in healthcare systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.