Clinical Research Directory

Pneumatic Tube System Versus Personnel Transport and the Hemolysis Index of Emergency Department Blood Samples: A Randomized, Split-Sample, Single-Blind Trial

Hemolysis is the most common pre-analytical error in emergency department (ED) laboratory specimens and can lead to false elevation of intracellular analytes (potassium, LDH, AST, hemoglobin), resulting in misdiagnosis and unnecessary testing. Blood samples in the ED are transported to the laboratory either by pneumatic tube systems (PTS) or manually by personnel. Although PTS shortens turnaround time, the forces generated during transport may damage erythrocyte membranes and promote hemolysis. Evidence on whether PTS increases hemolysis compared with personnel transport is inconsistent, partly because existing studies use parallel-group designs that cannot control for between-subject biological variability, and partly because findings differ across PTS brands and configurations. The Sumetzberger Power Control PTS installed at Marmara University Pendik Training and Research Hospital (speed 4-5 m/s, 120 m, cushioned capsule) has not been prospectively validated for hemolysis risk. This study uses a randomized, split-sample (within-patient matched), single-blind design in which two simultaneously drawn yellow-cap tubes from the same patient are randomly allocated, one to PTS and one to personnel transport, thereby eliminating between-patient variability. The primary outcome is the Hemolysis Index (HI) category (ordinal scale 0-5 corresponding to free hemoglobin thresholds of \<50, 50-99, 100-199, 200-299, 300-500, and \>500 mg/dL). Secondary outcomes include the rate of clinically significant hemolysis (HI \>= 1 / free Hb \>= 50 mg/dL) and the correlation between transport time and HI.

Smartphone AI Assistance for Prehospital ECG Interpretation

Prehospital providers interpret 12-lead electrocardiograms (ECGs) under time pressure and without immediate expert support. Missed acute coronary occlusion - occlusion myocardial infarction (OMI) - delays reperfusion, while false positive interpretations trigger unnecessary catheterization laboratory activations. Multimodal large language models (LLMs) available on any smartphone can now analyze a photographed ECG, and prehospital providers have begun using them spontaneously. No randomized trial has evaluated whether this practice improves diagnostic performance. This randomized controlled trial compares the diagnostic performance of prehospital providers interpreting ECG clinical vignettes with and without mandatory assistance from a single, version-locked smartphone large language model. Participants - paramedics, emergency medical technicians, nurses and physicians practicing in prehospital care in French-speaking Switzerland - are randomized 1:1 on a dedicated digital platform and answer 14 clinical vignettes presented in individually randomized order. Each vignette is built around a real, anonymized 12-lead ECG obtained during routine clinical care. The primary outcome is the proportion of vignettes for which the participant correctly identifies the presence or absence of an OMI. Secondary outcomes are sensitivity, specificity, and the accuracy of the prehospital priority decision level.

Achieving Diagnostic Excellence Through Prevention and Teamwork

This study seeks to link a group of hospitals to measure and share the rates of diagnostic errors, to understand underlying causes of diagnostic errors, and develop ways that hospitals, clinicians, and patients can work together to avoid diagnostic errors and harms due to those errors. The investigators will test how data sharing and collaboration improve diagnostic processes and develop approaches which can be sustained into the future. The approach represents a novel application of rigorous outcome adjudication to the problem of inpatient diagnostic errors using a learning health system model.

Diagnostic Accuracy of Two Large Language Models in Turkish Emergency Department Anamnesis Notes

This retrospective diagnostic accuracy study evaluates two large language models - GPT-4.1 (gpt-4.1-2025-04-14; OpenAI) and Claude Sonnet 4.6 (claude-sonnet-4-6; Anthropic) - as retrospective coding-quality instruments applied to anonymized Turkish-language emergency department anamnesis notes. The reference standard is the majority consensus of three board-certified emergency medicine specialists who independently coded each note in ICD-10, blinded to one another, to the code entered by the treating physician at case closure, and to the subsequent clinical course. Cases without chapter-level majority agreement are excluded without replacement. Both models are queried once per note with a single locked prompt at temperature 0 in stateless application programming interface calls, with no retrieval augmentation, no external tools and no extended-reasoning mode. The primary outcome is the proportion of cases in which each model's rank-1 diagnosis matches the reference standard at ICD-10 chapter level, reported with a Wilson 95% confidence interval. Registered secondary outcome measures are chapter-level Cohen's kappa between each model's rank-1 diagnosis and the reference standard; top-3 chapter accuracy for each model; and chapter-level concordance between the closure ICD-10 code and the reference standard. Additional prespecified analyses set out in the statistical analysis plan (paired between-model difference, three-character accuracy, note-length association, confidence calibration and model-to-model agreement) are reported in the primary publication. The ICD-10 code entered at case closure is characterised against the same reference standard as a description of current documentation practice; it is not a comparator, and no test of superiority or inferiority against model output is performed. The analysis plan was finalised and frozen before any accuracy computation. Reporting follows STARD-AI 2025.