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

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

Anterior shoulder dislocation, Biomechanical reduction techniques, Length-of-stay, Emergency
department, Reduction rate, No medication

Background To assess the effect of a commercial artificial intelligence (AI) solution implementation in the emer‐
gency 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 mor‐
tality 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).

Dmitry Kotovich1,2*, Gilad Twig1,2, Zeev Itsekson‐Hayosh3

, Maximiliano Klug4

, Asaf Ben Simon5
, Gal Yaniv4
,

Eli Konen4
, Noam Tau4

Dmitry Kotovich1,2*, Gilad Twig1,2, Zeev Itsekson‐Hayosh3

, Maximiliano Klug4

, Asaf Ben Simon5
, Gal Yaniv4
,

Eli Konen4
, Noam Tau4

, Daniel Raskin4

, Paul J. Chang6 and David Orion7

https://doi.org/10.1186/s12245-023-00523-y

2023

Peri Irawan

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