Preventing Delayed and Missed Care by Applying Artificial Intelligence to Trigger Radiology Imaging Follow-up
Jane Domingo, MS, MBA, Galal Galal, MD, MPH, Jonathan Huang, Priyanka Soni, MS, Vladislav Mukhin, MS, Camila Altman, MS, Tom Bayer, MS, Thomas Byrd, MD, Stacey Caron, RN, MSN, Patrick Creamer, et al.
Medical diagnostic imaging studies frequently detect findings that require further evaluation. An initiative at Northwestern Medicine was designed to prevent delays and improve outcomes by engineering reliable follow-up of radiographic findings. An artificial intelligence natural language processing (NLP) system was developed to identify radiology reports containing lung- and adrenal-related findings requiring follow-up. Over 13 months, more than 570,000 imaging studies were screened, of which more than 29,000 were flagged as containing lung-related follow-up recommendations, representing a 5.1% rate of lung-related findings occurrence on relevant imaging studies and an average of 70 findings flagged per day. Northwestern’s prospective clinical validation of the system, the first of its kind, demonstrated a sensitivity of 77.1%, specificity of 99.5%, and positive predictive value of 90.3% for lung findings requiring follow-up. To date, the workflow has generated nearly 5,000 interactions with ordering physicians and has tracked more than 2,400 follow-ups to completion. The authors conclude that NLP demonstrates significant potential to improve reliable follow-up to imaging findings and, thus, to reduce preventable morbidity in lung pathology and other high-risk and problem-prone areas of medicine.
Read Full Article Here: https://doi.org/10.1056/CAT.21.0469