Minimizing the Risk of Sample Mix-ups in the Molecular Pathology Section in Oncology Center Using Risk Assessment Matrix (RAM)

Abstract

Background: Sample mix-ups in molecular pathology can result in diagnostic errors, inappropriate treatment, and compromised patient safety. These risks are exacerbated in oncology settings, where accurate diagnoses directly impact patient outcomes. Purpose: This study aims to evaluate the effectiveness of the Risk Assessment Matrix (RAM) in minimizing the risk of sample mix-ups in the molecular pathology section of an oncology center. Methods: A prospective quality improvement design was adopted, comparing pre- and post-intervention data to assess the impact of RAM. Risks were identified through quality rounds and categorized using the RAM, where Likelihood (L) and Severity (S) scores were assigned to each risk (L × S = Risk Score). Interventions included automation, barcode labeling, revised workflows, and staff training. The effectiveness of interventions was measured through re-evaluation of risk scores and percentage risk reduction. Results: The interventions resulted in significant improvements across multiple areas. The risk score for Excel-based registration dropped from 16 to 2, representing an 88% reduction. Handwritten labeling errors decreased by 83%, and inaccurate documentation in LIS/HIS systems was reduced by 83%. Additionally, the risk associated with unattended PTS sample transport was lowered by 63%, and eliminating manual entry processes reduced errors by 67%. Most risks showed reductions above 60%, demonstrating the effectiveness of RAM in improving sample management and patient safety. Conclusion: The application of RAM in the molecular pathology section of an oncology center significantly reduced the likelihood of sample handling errors, enhancing both diagnostic accuracy and patient safety. The study highlights the importance of automation, real-time monitoring, and multidisciplinary collaboration in sustaining these improvements. RAM provides a structured framework for prioritizing and mitigating risks in complex healthcare workflows.

Ähnliche Arbeiten