Analysis of errors in histology by root cause analysis: a pilot study

Medical errors in laboratory settings remain a significant concern, particularly in complex systems like Anatomic Pathology. Prior research has primarily focused on diagnostic errors, leaving gaps in understanding issues within routine histology preparation. Established knowledge indicates that human systems are prone to errors, yet the pre-analytical phase in histology has not been thoroughly examined. This paper addresses a knowledge gap by exploring the root causes of errors in histology processes. The study builds on prior work that identified errors but did not investigate their origins. By focusing on the histology workflow, the authors aim to uncover patterns and sources of mistakes. No prior work had resolved how to systematically analyze these errors. This gap motivated the use of root cause analysis to identify and categorize errors in each phase of histology preparation. The study introduces a new approach to understanding and mitigating these errors.

The study aimed to evaluate the pre-analytical histology process in an Anatomic Pathology laboratory to identify and analyze errors. The authors sought to determine the frequency and sources of errors in each stage of histology preparation. By applying root cause analysis, they hoped to uncover the underlying reasons for these errors. This approach allows for a structured examination of how and why errors occur. The study's motivation stems from the recognition that errors are common in complex systems like pathology. The authors wanted to move beyond identifying errors to understanding their causes. This pilot study was designed to test the effectiveness of root cause analysis in a clinical setting. The ultimate goal is to improve procedural safety and reduce error rates in histology workflows.

The researchers used root cause analysis to examine errors in histology preparation. They first defined a list of potential errors that could occur in the process. A trained technician tracked and recorded errors over a three-month period. The study reviewed 8,346 histological cases, with 19,774 samples and 29,956 histologies prepared. Errors were categorized by phase of the histology process. The 'fishbone' diagram and 'five whys' methods were used to analyze each error. These tools helped identify the root causes of each issue. The study focused on errors in accessioning, gross dissecting, processing, and other stages.

A total of 132 errors were identified across all phases of histology preparation. Accessioning had the lowest error rate at 6.5%, while gross dissecting had the highest at 28%. Labeling and releasing had an error rate of 35%, the highest among all phases. Tissue cutting and slide mounting had an error rate of 23%. Processing and coloring had the lowest rates at 1.5% each. These findings suggest that certain stages are more prone to errors than others. The use of root cause analysis revealed multiple contributing factors to each error. The study shows that errors often result from multiple causes rather than single failures.

The study demonstrates that root cause analysis is an effective tool for identifying and understanding errors in histology processes. The authors conclude that errors in histology preparation are often due to multiple causes rather than single failures. The analysis revealed that certain phases, like gross dissecting and labeling, are more error-prone. The study suggests that targeted training can help operators recognize their role in risk management. The authors propose that improving awareness can lead to better procedural safety. Root cause analysis is presented as a practical method for clinical risk management. The findings suggest that addressing the most frequent error points can improve overall workflow safety. The study supports the use of root cause analysis in identifying and preventing errors in complex systems like Anatomic Pathology.