Preventing Delayed and Missed Care by Applying Artificial Intelligence to Trigger Radiology Imaging Follow-up

Abstract

SummaryMedical 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.

Ähnliche Arbeiten