Related Experiment Video
Updated: Jan 9, 2026

A Teleoperated Robotic System-Assisted Percutaneous Transiliac-Transsacral Screw Fixation Technique
Published on: January 6, 2023
Uncovering safety risks across multispecialty: A Human Factor Analysis and Classification System (HFACS) based
Asfand Khan1, Tara Cohen2, Scott A Shappell3
1Department of Human Factors and Behavioral Neurobiology, Embry Riddle Aeronautical University, Project Manager at AdventHealth, Embry Riddle Aeronautical University, Daytona Beach, Florida, USA.
Abstract:
Despite significant progress in patient safety, human error continues to occur at high rates in surgical settings. The Human Factors Analysis and Classification System (HFACS) offers a proactive lens to understand how and where errors emerge. This study examines HFACS's utility and reliability in categorizing and comparing human error in cardiovascular, orthopedic, trauma care, and neurosurgery. Observational data from cardiovascular, orthopedic, trauma, and neurosurgery cases were coded using HFACS by trained analysts applying unanimous, majority, and reconciled consensus strategies to assess interrater reliability. Across specialties, 98.25% of disruptions occurred at the "preconditions for unsafe acts," indicating latent failures. In cardiovascular surgery, 49.20% were linked to adverse mental states (e.g., cognitive overload, stress), 26.95% to physical environment issues, and 12.69% to crew resource management. Orthopedic surgery showed 68.75% of crew resource management failures, 19.47% personal readiness issues, and 5.87% environment stressors. Trauma care involved 61.38% crew resource management, 26.71% adverse mental states, and 10.33% team availability. Neurosurgery disruptions stemmed 59.42% from technological environment/layout and 35.92% from communication, coordination, and planning. HFACS is a reliable tool for categorizing human factors in diverse surgical environments. Findings highlight distinct latent failure profiles across specialties and underscore the importance of data driven specialty-specific safety interventions.

