A Prospective Investigation to Develop Data-Driven Interventions and Improve Process Efficiency at a Level II Trauma Center

INTRODUCTION: The purpose of this investigation was to better understand process inefficiencies in a Level II trauma center through the identification and classification of flow disruptions. Data-driven interventions were systematically developed and introduced in an effort to reduce disruptions threatening the optimal delivery of trauma care. METHOD: Medical human factors researchers observed disruptions during resuscitation and imaging in 117 trauma cases. Data was classified using the human factors taxonomy Realizing Improved Patient Care through Human-centered Operating Room Design for Threat Window Analysis (RIPCHORD-TWA). Interdisciplinary subject matter experts (SMEs) utilized a human factors intervention matrix (HFIX) to generate targeted interventions designed to address the most detrimental disruptions. A multiple-baseline interrupted time-series (ITS) design was used to gauge the effectiveness of the interventions introduced. RESULTS: Significant differences were found in the frequency of disruptions between the pre-intervention (n=65 cases, 1137 disruptions) and post-intervention phases (n=52 cases, 939 disruptions). Results revealed significant improvements related to ineffective communication (x2 (1, n=2076) = 24.412, p=0.00, x2 (1, n=1031) = 9.504, p=0.002, x2 (1, n=1045) = 12.197, p=0.000); however, similar levels of improvement were not observed in the other targeted areas. CONCLUSION: This study provided a foundation for a data-driven approach to investigating precursor events and process inefficiencies in trauma care. Further, this approach allowed individuals on the front lines to generate specific interventions aimed at mitigating systemic weaknesses and inefficiencies frequently encountered in their work environment