The impact on clinical outcomes after 1 year of implementation of an artificial intelligence solution for the detection of intracranial hemorrhage

• Published in: International journal of emergency medicine Vol. 16; no. 1; p. 50
• Main Authors: Kotovich, Dmitry, Twig, Gilad, Itsekson-Hayosh, Zeev, Klug, Maximiliano, Simon, Asaf Ben, Yaniv, Gal, Konen, Eli, Tau, Noam, Raskin, Daniel, Chang, Paul J., Orion, David
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 11.08.2023; Springer Nature B.V; BMC
• Subjects: Angiology; Artificial intelligence; Cardiology; Clinical outcomes; Emergency medical care; Emergency Medicine; Internal Medicine; Intracranial hemorrhage; Medicine; Medicine & Public Health; Modified ranking scale; Mortality; Pediatrics; Trauma centers; Intracranial hemorrhage; Artificial intelligence; Modified ranking scale
• ISSN: 1865-1372; 1865-1380; 1865-1380
• DOI: 10.1186/s12245-023-00523-y

Background 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. The ICH algorithm was applied to 587 consecutive patients with a confirmed diagnosis of ICH on head CT upon admission to the emergency department. 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. The secondary outcome was morbidity based on Modified Rankin Scale for Neurologic Disability (mRS) at discharge. Results Five hundred eighty-seven 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 were significantly 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 The added value of this study emphasizes the introduction of artificial intelligence (AI) computer-aided triage and prioritization software in an emergent care setting that demonstrated a significant reduction in a 30- and 120-day all-cause mortality and morbidity for patients diagnosed with intracranial hemorrhage (ICH). Along with mortality rates, the AI software was associated with a significant reduction in the Modified Ranking Scale (mRs). Key points The 30-day and 120-day post-discharge all-cause mortality was observed to be significantly lower in the post-artificial intelligence (post-AI) implementation period compared to pre-AI period (27.7% vs 17.5% p  = 0.004; 31.8% vs 21.7% p  = 0.017, respectively). Modified Rankin Scale (mRS) for neurological disability at discharge was significantly reduced post-AI implementation (3.2 vs 2.8; p  = 0.044).