Project Details

Early Career

Status: Funded - Open

Clinical Decision Support for Managing Children with Mild Head Injuries and Intracranial Injury

Jacob Greenberg, MD, MSCI

Summary

BACKGROUND: Pediatric traumatic brain injury TBI (TBI) leads to approximately 600,000 emergency departments each year in the United States, with 90% of new diagnoses considered mild (mTBI). Among children with mTBI, the acute evaluation is primarily focused on identifying children with intracranial injury who may be at increased risk of neurological decline.

GAP: While substantial attention has been devoted to developing clinical decision support to guide the need for CT imaging in children with mTBI, far less effort has been dedicated to stratifying the risk of neurological decline among children with intracranial injury on CT. The Children’s Intracranial Injury Decision Aid (CHIIDA) score represents one promising tool to risk-stratify this population.

HYPOTHESIS: The CHIIDA score can reliably predict the risk of neurological among children with mTBI and intracranial injury. Through integration into the electronic health record, this predictive tool can improve the safety and efficiency of managing these patients.

METHODS: A multicenter dataset will be used to externally validate the ability of the CHIIDA score to predict the risk of neurological decline among children with mTBI and intracranial injury. Employing both sociotechnical analysis and usability evaluation techniques, this study will evaluate the implementation context for and develop a prototype of electronic clinical decision support based on this predictive tool.

RESULTS: Pending

IMPACT: External validation of the CHIIDA score will be a critical step in establishing its clinical utility, and the development and evaluation of a prototype electronic clinical decision support tool will facilitate the clinical integration of this risk model. Together, these efforts will enable a future multicenter randomized trial to definitively demonstrate the improved safety and resource-efficiency of utilizing this evidence-based decision guide.