Abstract
OBJECTIVES: We investigated the impact of clinical guidelines for the management of minor head injury on utilization and diagnostic yield of head CT over two decades. METHODS: Retrospective before-after study using multiple electronic health record data sources. Natural language processing algorithms were developed to rapidly extract indication, Glasgow Coma Scale, and CT outcome from clinical records, creating two datasets: one based on all head injury CTs from 1997 to 2009 (n = 9109), for which diagnostic yield of intracranial traumatic findings was calculated. The second dataset (2009-2014) used both CT reports and clinical notes from the emergency department, enabling selection of minor head injury patients (n = 4554) and calculation of both CT utilization and diagnostic yield. Additionally, we tested for significant changes in utilization and yield after guideline implementation in 2011, using chi-square statistics and logistic regression. RESULTS: The yield was initially nearly 60%, but in a decreasing trend dropped below 20% when CT became routinely used for head trauma. Between 2009 and 2014, of 4554 minor head injury patients overall, 85.4% underwent head CT. After guideline implementation in 2011, CT utilization significantly increased from 81.6 to 87.6% (p = 7 × 10(-7)), while yield significantly decreased from 12.2 to 9.6% (p = 0.029). CONCLUSIONS: The number of CTs performed for head trauma gradually increased over two decades, while the yield decreased. In 2011, despite implementation of a guideline aiming to improve selective use of CT in minor head injury, utilization significantly increased. KEY POINTS: • Over two decades, the number of head CTs performed for minor, moderate, and severe head injury gradually increased, while the diagnostic yield for intracranial findings showed a decreasing trend. • Despite the implementation of a guideline in 2011, aiming to improve selective use of CT in minor head injury, utilization significantly increased, while diagnostic yield significantly decreased. • Natural language processing is a valuable tool to monitor the utilization and diagnostic yield of imaging as a potential quality-of-care indicator.