June 5, 2019
Effects of Time Pressure and Experience on Procedural Performance: Can we use Machine Learning to predict this?
Camille Peres, Asst. Professor, Environmental Health, TX A & M University
Well-written procedures can be an integral part of safe operation, managing risks, and continuous improvement in the high-risk settings of process industries. Although the importance of procedures is recognized by all industries in general, significant incidents have occurred in past due to procedural systems not being designed to support human performance. This presentation will share results from a series of studies investigating the effects of time pressure and experience on procedural performance. Further, findings regarding how machine learning may be able to facilitate the translation of Human Factors research to specific design criteria for procedures and procedural systems. Potential applications of these findings will be discussed.