Lean Six Sigma: Application of the Methodology in Data Processing for Cancer Registry

Abstract

Since 2020, the Catania-Messina-Enna Cancer Registry (CR) has operated a transformational and incremental program while also applying a Lean Six Sigma methodology (LSS) to optimize the processes and reduce waste. Each project aimed to raise the performance of the CR while also providing the opportunity for human resources to express their talent. In this context, a machine learning project was developed to reduce the time spent on raw free-text histopathological reports that contain relevant information for cancer evaluation. The ability to extract meaningful information from histopathology reports is really important because reports provide crucial insights into the morphology and topography of cancer, enabling operators to validate oncology cases with the utmost diagnostic precision. However, the CR faced a significant challenge due to the extensive volume of written natural language reports, where only a small fraction contains pertinent information for cancer evaluation. In this paper, we describe how we applied the LSS method, the observed difficulties, and the benefits achieved by adopting a machine learning algorithm as a strategic solution in the Improve phase.

Keywords

• project management
• Lean Six Sigma
• cancer registry