DEEP SEE™—A Seven-Step Framework for Deeper, Bias-Aware Root Cause Analysis in Healthcare

Abstract

Root Cause Analysis (RCA) remains the primary investigative tool for adverse events in healthcare, yet its limitations are increasingly recognised. Many analyses fail to account for cognitive biases, cultural influences, and complex system interdependencies, resulting in incomplete learning and weak corrective actions. This paper introduces DEEP SEE™, a seven-step framework designed to move beyond linear cause-and-effect thinking. By guiding investigators from surface-level descriptions to deeper cultural and contextual insights, DEEP SEE™ supports richer understanding and more actionable recommendations. The model is illustrated through eight representative cognitive bias scenarios adapted from real-world Morbidity & Mortality (M&M) and incident review contexts. While not a formal research evaluation, DEEP SEE™ offers a structured, bias-aware approach that can be integrated into existing patient safety review processes and provides a foundation for future empirical study.