B-033 Microbiology laboratory improvements with Gram staining automated implementation

Abstract The Gram staining technique, developed in the late 19th century, remains a key method for bacterial identification in microbiology. This procedure divides bacteria into Gram-positive and Gram-negative groups and allows for distinguishing cellular structures such as cocci, bacilli, etc. Traditional methods are prone to human error and heavily rely on the operator’s expertise. Therefore, automating the proceedings with equipment that employs technology to standardize it would optimize the process, reducing time and improving accuracy. Methods: This study utilized clinical samples and ATCC bacterial strains, processed at the National Reference Center of Valle de México. Two methods were compared: manual and automated, evaluating three main criteria:Staining times, analyzed using a Value Stream Mapping (VSM).Staining quality, assessed parameters such as homogeneity, structure definition, and the absence of precipitated reagents.Reproducibility, analyzed using 20 samples of Escherichia coli ATCC 25922, 20 samples of Enterococcus faecalis ATCC 29212, and 20 clinical samples per method, using 2x2 contingency tables to determine concordance. Results: The VSM analysis showed that the manual method requires 12.48 minutes to process 30 samples (24.6 seconds per sample), whereas the automated method reduces the time to 7.7 minutes (15 seconds per sample). This translates to a 39% reduction in process time. The automated system provided superior staining quality, with better homogeneity and clearer definition of cellular structures, as well as a reduction in pre-analytical errors. The results were reproducible and comparable for both methods, ensuring the accuracy of the automated process. Conclusion: Automating the Gram staining process offers multiple advantages, such as time savings, result standardization, and reduced human error. The automated system optimizes the staining process by reducing processing times and enhancing the quality of observed structures. While both methods are comparable in terms of reproducibility, the automated method demonstrates a clear advantage in efficiency and precision, making it ideal for laboratories with high sample volumes or those aiming to optimize time and minimize pre-analytical errors.