San Francisco-based healthcare system Sutter Health, a not-for-profit covering Northern California, recently announced it’s expanding a pilot with startup Ferrum Health using the company’s AI-powered quality system to assist radiologists and catch preventable medical errors in patients with lung cancer.
Ferrum’s system analyzes medical images as well as physician written notes, using computer vision and natural language algorithms to identify abnormalities and flag them for clinicians to follow-up with patients and change care plans as necessary.
“For those rare instances where a nodule is overlooked, we can in a very quick time period—usually within 24 hours—continue the care process instead of waiting until larger nodules are detected at a later medical visit,” said Charles McDonnell III, MD, a Sutter Medical Group radiologist and associate medical director of risk management, in a statement. “Having a system for quality coverage of our diagnostic decisions makes us stronger, more effective advocates for our patients, and gives patients greater comfort and peace of mind.”
While Ferrum is an AI company, it focuses on deploying existing algorithms as a background-monitoring “safety net” that only requires active attention from radiologists when it detects a discrepancy with their diagnoses.
“Coming out of my previous company and the strength that we had working with healthcare systems like Sutter we really realized that AI could be used in a few different ways, but that physicians, given all the pressures that they were under, were going to really struggle to carve out time in their workflow, in their day-to-day, to deploy solutions that are really focused on improving quality,” explained founder Pelu Tran, who previously founded and led Google Glass company Augmedix. “So we thought that there was a better approach to trying to help them care for their patients. And that was really by providing solutions that improve quality without disrupting workflow. And we did it here by deploying a monitoring system, a tool that runs in the background and and looks for these errors at a population enterprise level without really disrupting or requiring physician behavior change.”
The initial deployment is focused specifically on finding nodules that can show up in the early stages of lung cancer. In the first 90 days, the technology has already reviewed 10,000 CT scans containing lung tissue and found 83 discrepancies that then prompted additional review and intervention by radiologists.