Using AI to Ensure Follow-Up of Suspicious Nodules in Radiology Reports

Nearly every day exciting articles are published about how AI is being used in pathology and radiology to improve the quality of reporting and the follow-up of salient findings. A recent article discussed such AI software (see:How University of Rochester uses AI to reduce risk of failed follow-up). Below is an excerpt from it:TheUniversity of Rochester Medical Center needed a better way to ensure that its many patients with incidental radiology findings received their recommended follow-up care in a timely manner. Failure to follow up happens for a number of reasons, including inconsistent communications during care transitions, not notifying patients of actionable test results, and inadequate systems for managing and tracking incidental findings.....In 2015, the provider organization piloted a recommendation tracking system it calls “Backstop”....The goal of the system was to serve as a safety net for patients for whom clinicians had identified a potential malignancy or aneurysm and offered an actionable recommendation....[However,] the manual process required to flag recommendations was a significant barrier to widespread adoption of the program.The Backstop program depended on the radiologist manually adding patient cases to a central database for tracking at the time of dictation.....Backstop needed to take advantage of recent advances in natural language processing algorithms to help identify and track more of the recommendations coming out of the department (see:A B...
Source: Lab Soft News - Category: Laboratory Medicine Authors: Tags: Diagnostics Healthcare Information Technology Healthcare Innovations Lab Industry Trends Medical Research Pathology Informatics Radiology Surgical Pathology Source Type: blogs