The impact on clinical outcomes after one year of implementation of an artificial intelligence solution for the detection of intracranial hemorrhage

Abstract

Abstract Objectives: To assess the effect of a commercial Artificial Intelligence (AI) solution implementation in the emergency department on clinical outcomes in a single Level 1 Trauma Center. Methods: A retrospective cohort study for two time periods – Pre-AI (1.1.2017-1.1.2018) and Post-AI (1.1.2019-1.1.2020), in a Level 1 Trauma Center was performed. Participants older than 18 years with a confirmed diagnosis of ICH on head CT upon admission to the emergency department were collected. Study variables included demographics, patient outcomes, and imaging data. Participants admitted to the emergency department during the same time periods for other acute diagnoses (ischemic stroke –IS; and myocardial infarction - MI) served as control groups. Primary outcomes were 30- and 120-day all-cause mortality. Secondary outcome was morbidity based on Modified Rankin Scale for Neurologic Disability (mRS) at discharge. Results: 587 participants (289 Pre-AI – age 71 ± 1, 169 men; 298 Post-AI – age 69 ± 1, 187 men) with ICH were eligible for the analyzed period. Demographics, comorbidities, Emergency Severity Score, type of ICH and length of stay were not significantly different between the two time periods. The 30- and 120-day all-cause mortality weresignificantly reduced in the Post-AI group when compared to the Pre-AI group (27.7% vs 17.5%; p =0.004 and 31.8% vs 21.7%; p =0.017 respectively).Modified Rankin Scale (mRS) at discharge was significantly reduced Post-AI implementation (3.2 vs 2.8; p =0.044). Conclusion: Implementation of an AI based computer aided triage and prioritization solution for flagging participants with ICH in an emergent care setting coincided with significant reductions of 30- and 120-day all-cause mortality and morbidity.

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