Trial reportThe Lancet. Digital health2025
Diagnoses supported by a computerised diagnostic decision support system versus conventional diagnoses in emergency patients (DDX-BRO): a multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial.
Trial report in The Lancet. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05346523 (Effects of Digitalized Differential Diagnosis Broadening Using a Computerized Diagnostic Decision Support Tool on Diagnostic Quality in Emergency Room Patients - a Multi-centre Cluster Randomized Cross-over Trial.), which is not on this map. Cited by 9 papers.
What it found
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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.
Effects of Digitalized Differential Diagnosis Broadening Using a Computerized Diagnostic Decision Support Tool on Diagnostic Quality in Emergency Room Patients - a Multi-centre Cluster Randomized Cross-over Trial.
Who cites it
9 citing papers in PubMed.
- Identification of diagnostic discrepancies as a quality assurance measure in emergency medicine - a validation study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Trial
- Impact of a Symptom Checker App on Patient-Physician Interaction Among Self-Referred Walk-In Patients in the Emergency Department: Multicenter, Parallel-Group, Randomized, Controlled Trial.Journal of medical Internet research · 2025Trial
- Incidence, contributing factors, and predictors of diagnostic errors in medical inpatients: A retrospective cohort study.Journal of hospital medicine · 2026Article
- CATalyze-AI: accelerating referral for thrombectomy in acute stroke patients using an AI-based software. A prospective, multicentre, quasi-experimental cohort study.European stroke journal · 2026Article
- Effectiveness of Guideline-Based Clinical Decision Support Systems: Protocol for a Systematic Review.JMIR research protocols · 2026Article
- The Cardiologist Driving Synthetic AI: The TIMA Method for Clinically Supervised Synthetic Data Generation.Journal of clinical medicine · 2026Article
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Evaluation of diagnostic accuracy using routine laboratory parameters.Bioinformation · 2026Article
- Piloting Temperature-Driven Variability in Emergency Diagnostic Accuracy Using a Leading Large Language Model.Cureus · 2025Article
Corrections and comments
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnostic error is a frequent and clinically relevant health-care problem. Whether computerised diagnostic decision support systems (CDDSSs) improve diagnoses is controversial, and prospective randomised trials investigating their effectiveness in routine clinical practice are scarce. We hypothesised that diagnoses made with a CDDSS in the emergency department setting would be superior to unsupported diagnoses.
methodsThis multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial was done in four emergency departments in Switzerland. Eligible patients were adults (aged ≥18 years) presenting with abdominal pain, fever of unknown origin, syncope, or non-specific symptoms. Emergency departments were randomly assigned (1:1) to one of two predefined sequences of six alternating periods of intervention or control. Patients presenting during an intervention period were diagnosed with the aid of a CDDSS, whereas patients presenting during a control period were diagnosed without a CDDSS (usual care). Patients and personnel assessing outcomes were masked to group allocation; treating physicians were not. The primary binary outcome (false or true) was a composite score indicating a risk of reduced diagnostic quality, which was deemed to be present if any of the following occurred within 14 days: unscheduled medical care, a change in diagnosis, an unexpected intensive care unit admission within 24 h if initially admitted to hospital, or death. We assessed superiority of supported versus unsupported diagnoses in all consenting patients using a generalised linear mixed effects model. All participants who received any study treatment (including control) and completed the study were included in the safety analysis. This trial is registered with ClinicalTrials.gov (NCT05346523) and is closed to accrual.
findingsBetween June 9, 2022, and June 23, 2023, 15 845 patients were screened and 1204 (591 [49·1%] female and 613 [50·9%] male) were included in the primary efficacy analysis. The median age of participants was 53 years (IQR 34-69). Diagnostic quality risk was observed in 100 (18%) of 559 patients with CDDSS-supported diagnoses and 119 (18%) of 645 with unsupported diagnoses (adjusted odds ratio 0·96 [95% CI 0·71-1·3]). 94 (7·8%) patients suffered a serious adverse event, none related to the study.
interpretationUse of a CDDSS did not reduce the occurrence of diagnostic quality risk compared with the usual diagnostic process in adults presenting to emergency departments. Future research should aim to identify specific contexts in which CDDSSs are effective and how existing CDDSSs can be adapted to improve patient outcomes.
fundingSwiss National Science Foundation and University Hospital Bern.
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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.