Abstract

Morbidity and mortality data (MMD), as the traditional measure of surgical performance, have major limitations when used to assess and ensure quality of surgical performance. To improve and ensure the safest possible surgical performance, there is a need for prospective observational multidisciplinary studies, for which surgeons and human factor specialists should work together towards this objective. These considerations have led to the development of new systematic approaches for assessing and improving surgical operative performance. One of these is human reliability analysis (HRA), which eventually progressed to observational clinical human reliability analysis (OCHRA). HRA techniques are widely used in the risk management of safety-critical systems, e.g. nuclear power industry, aviation industry, and military operations. HRA techniques determine the impact of human error within a system. Surgical complications are related to techniques and result from errors most commonly committed during the intervention. Therefore, these errors can be influenced, i.e. deducted, by an HRA system that proactively reduces risk by preventing errors during human activities to the ‘as low as reasonably possible’. Two major limitations of OCHRA are its labour-intensive nature and the requirement for human factors engineering expertise in the assessment. These issues will be resolved in the short term by the significant progress based on artificial intelligence and machine learning, alongside with increased clinical use of OCHRA in surgical practice and health care in general.