1495
clinical study

Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography

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  • Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography

Materials & Methods

An ML algorithm was incorporated across CT scanners at imaging sites in January 2018. RTAT and LOS were derived for reports and patients between July 2017 and December 2017 prior to implementation of ML and compared to those between January 2018 and June 2018 after implementation of ML. A total of 25,658 and 24,996 ED and inpatient cases were evaluated across the entire healthcare system before and after ML, respectively.

Results

Inpatient LOS for positive cases decreased from 6.91 days to 6.16 days (p>.05). ED LOS decreased by 59.10 minutes, from 566.56 minutes to 507.56 minutes (p<.001).

Conclusions

The clinical application of AI platform for the detection of ICH significantly decreased LOS, indicating that patients were efficiently triaged to the appropriate care.

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