Evaluating the Completeness of Histopathology Sample Labeling

Background and Aim: A major quality assurance target is minimizing error rates to enhance patient safety. Clinical laboratories have long focused their attention on quality control methods and quality assessment programs. The current study aimed to investigate the completeness of specimen labeling in the histopathology department. Materials and Methods: In this cross-sectional retrospective study, we analyzed labels of tissue samples sent from Orthopedics, Neurosurgery, Internal medicine, Urology, ENT, Surgery, ICU, and Endoscopy wards over three months in Urmia Imam Khomeini hospital from January 2023 to March 2023. All information including the patient's name, age, patient file number, tissue type, anatomical location, sampling date, name of the referring physician, legibility of written information, two patient identifiers (full name, date of birth or file number), and affixing the label on the body of the container was checked. Results: Of the 2178 pathology samples examined, the largest numbers of samples (53.58%) were sent from the surgery department and the least (0.69%) from the neurosurgery Ward. The lowest number of errors was related to the patient's age, which ranged from 0% in the internal medicine, neurosurgery and, endoscopy to 8.5% in the urology Ward. There was a statistically significant difference between the criteria obtained in different wards, except for the patient's name and illegibility of written information. P-value was <0.001 for patient age, file number, tissue type, sampling date, doctor's name, label on container body and two-identifier registration, and 0.049 for the anatomical position of the sample. Conclusion: This study showed that there is a variety of errors in labeling pathology samples. This shows that the high volume of work and a large number of samples cannot be the reason for the error in the labeling. Due to the similarity of names, two samples may be mistaken for each other, but the presence of the second identifier solves this problem. Newer technologies such as bar coding may reduce the frequency of specimen labeling errors.