Abstract:
Background: The Gram staining procedure, a fundamental microbiological technique established in 1884, is crucial for bacterial identification and classification. Despite its significance, there is an ongoing need to enhance laboratory efficiency and reduce diagnostic turnaround times. Lean strategies, originating from Taiichi Ohno's work with Toyota, emphasize waste elimination and process improvement, offering a promising approach to optimizing laboratory operations.
Objective: This study aims to evaluate the impact of implementing Lean strategies on the efficiency of Gram staining procedures within a microbiology laboratory, focusing on reducing turnaround times and improving diagnostic precision without compromising quality.
Methods: A cross-sectional comparative study was conducted at the Department of Pathology, Combined Military Hospital, Lahore, from September to December 2022. Utilizing a non-probability convenience sampling technique, 122 samples were analyzed pre and post Lean implementation. Lean methodologies, including Just-In-Time inventory management and standardized work procedures, were applied to optimize sample transport, accessioning, processing, and staining phases. The study employed SPSS version 25 for statistical analysis, using the chi-square test to compare efficiencies, with a significance level set at p<0.05.
Results: Post-implementation of Lean strategies, significant reductions in non-value-added times were observed across all stages of the Gram staining process. Transport and accessioning times were reduced from 20 and 48 minutes to 15 and 30 minutes, respectively. The staining process time decreased from 121 to 104 minutes, and microscopy time was reduced from 201 to 56 minutes. Overall, the study reported a drastic decrease in non-value-added time from 825 to 56 minutes, maintaining the quality of staining results.
Conclusion: The application of Lean strategies to the Gram staining procedure significantly enhances laboratory efficiency by minimizing waste and optimizing process times. This approach not only improves diagnostic turnaround times but also maintains high-quality standards, suggesting a scalable model for broader healthcare system improvements.
