Threat and error management for anesthesiologists

Abstract

Purpose of review Patient care in the operating room is a dynamic interaction that requires cooperation among team members and reliance upon sophisticated technology. Most human factors research in medicine has been focused on analyzing errors and implementing system-wide changes to prevent them from recurring. We describe a set of techniques that has been used successfully by the aviation industry to analyze errors and adverse events and explain how these techniques can be applied to patient care. Recent findings Threat and error management (TEM) describes adverse events in terms of risks or challenges that are present in an operational environment (threats) and the actions of specific personnel that potentiate or exacerbate those threats (errors). TEM is a technique widely used in aviation, and can be adapted for the use in a medical setting to predict high-risk situations and prevent errors in the perioperative period. A threat taxonomy is a novel way of classifying and predicting the hazards that can occur in the operating room. TEM can be used to identify error-producing situations, analyze adverse events, and design training scenarios. Summary TEM offers a multifaceted strategy for identifying hazards, reducing errors, and training physicians. A threat taxonomy may improve analysis of critical events with subsequent development of specific interventions, and may also serve as a framework for training programs in risk mitigation.

Identifying Critical Contextual Design Cues Through a Machine Learning Approach

“…Such problems where human operators of complex automated systems are confused by the displays and do not understand the communications from the automation are widespread, occurring in domains such as anesthesiology (Ruskin, Stiegler, Clark, and Guffey 2013), commercial aircraft control (Vakil and Hansman 2002), and military weapons systems operation (Cummings 2004). This problem is so well documented that it has been termed mode confusion , which occurs when an operator's mental model differs from the automation's behavior (Bredereke and Lankenau 2002).…”

Human factors in anesthesia: Risk assessment and clinical decision-making

“…3 Incorporation of checklists into perioperative practice may be beneficial prior to invasive procedures, hand-offs or changes in the phase of care (induction, transit, post-op etc). 9 …”

A proposed system for standardization of colour-coding stages of escalating criticality in clinical incidents

“…In TEMs, pilots are taught to ‘avoid’ ‘trap’ and ‘mitigate’ errors in order to prevent an ‘undesirable aircraft state’ and, ultimately, an aviation accident. The TEMs model has also been adapted in the USA for anaesthesiology practice [ 33 ]. Colour does not feature prominently in the model, although in at least one graphical depiction ‘avoid’ is yellow, ‘trap’ is amber and ‘mitigate’ is red [ 34 ].…”