Diagnostic Errors

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, 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.

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.