A Data-Driven Approach to Team Training for Nurses in a Level II Trauma Center.

This prospective investigation describes the process of designing a targeted, data-driven team training aimed at reducing identified process inefficiencies or flow disruptions (FDs) that threaten the optimal delivery of trauma care. Trained researchers observed and classified FDs during 34 trauma cases in a Level II trauma center. Multidisciplinary trauma personnel generated interventions to identified issues using the human factors intervention matrix (HFIX). This article focuses on one intervention: a formal trauma nurse training program centered around leadership, teamwork, and communication. The training was well perceived and was found to have a significant impact on participant knowledge of course content; t (65) = -13.92, p ≤ .01. By using hospital-specific data to drive intervention development from multidisciplinary team members, it is possible to develop effective solutions aimed at addressing individual threats.

Using Broken Windows Theory as the Backdrop for a Proactive Approach to Threat Identification in Health Care.

OBJECTIVES: Historically, health care has relied on error management techniques to measure and reduce the occurrence of adverse events. This study proposes an alternative approach for identifying and analyzing hazardous events. Whereas previous research has concentrated on investigating individual flow disruptions, we maintain the industry should focus on threat windows, or the accumulation of these disruptions. This methodology, driven by the broken windows theory, allows us to identify process inefficiencies before they manifest and open the door for the occurrence of errors and adverse events. METHODS: Medical human factors researchers observed disruptions during 34 trauma cases at a Level II trauma center. Data were collected during resuscitation and imaging and were classified using a human factors taxonomy: Realizing Improved Patient Care Through Human-Centered Operating Room Design for Threat Window Analysis (RIPCHORD-TWA). RESULTS: Of the 576 total disruptions observed, communication issues were the most prevalent (28%), followed by interruptions and coordination issues (24% each). Issues related to layout (16%), usability (5%), and equipment (2%) comprised the remainder of the observations. Disruptions involving communication issues were more prevalent during resuscitation, whereas coordination problems were observed more frequently during imaging. CONCLUSIONS: Rather than solely investigating errors and adverse events, we propose conceptualizing the accumulation of disruptions in terms of threat windows as a means to analyze potential threats to the integrity of the trauma care system. This approach allows for the improved identification of system weaknesses or threats, affording us the ability to address these inefficiencies and intervene before errors and adverse events may occur.

Proactive Safety Management in Trauma Care: Applying the Human Factors Analysis and Classification System.

INTRODUCTION: This article examines the reliability of the Human Factors Analysis and Classification System (HFACS) for classifying observational human factors data collected prospectively in a trauma resuscitation center. METHODS: Three trained human factors analysts individually categorized 1,137 workflow disruptions identified in a previously collected data set involving 65 observed trauma care cases using the HFACS framework. RESULTS: Results revealed that the framework was substantially reliable overall (κ = 0.680); agreement increased when only the preconditions for unsafe acts were investigated (κ = 0.757). Findings of the analysis also revealed that the preconditions for unsafe acts category was most highly populated (91.95%), consisting mainly of failures involving communication, coordination, and planning. CONCLUSION: This study helps validate the use of HFACS as a tool for classifying observational data in a variety of medical domains. By identifying preconditions for unsafe acts, health care professionals may be able to construct a more robust safety management system that may provide a better understanding of the types of threats that can impact patient safety.