In this podcast, a medical expert talks about how physicians can avoid diagnostic errors -- a top threat to patient safety -- by using AI-based clinical decision support software.
Art Papier, M.D., a practicing dermatologist and medical school professor, has spent decades working on using health IT to prevent diagnostic errors in medicine.
Papier is also CEO and co-founder of VisualDx, a clinical decision support software vendor. The VisualDx platform uses machine learning to enhance diagnostic accuracy, aid therapeutic decisions and improve patient safety.
In this podcast, Papier talks about how physicians can reduce diagnostic errors in medicine by augmenting their diagnoses and treatment with technology.
In spite of advances in health IT and medical hardware, the ECRI Institute ranked medical diagnostic error at the top of its 2018 list of threats to patient safety.
"It has been a problem that has been around since we have practiced medicine, and software has been developed to improve diagnosis probably for 40 or 50 years but has not been widely used," Papier says in the podcast.
"Now we're seeing a shift in awareness of the problem of diagnostic errors and also new technologies that are coming online to really improve diagnosis," he says.
However, Papier notes that the patient quality and safety movement has not been focused so much on technology and diagnostic errors in medicine as on hospital safety issues such as slips and falls and hand-washing to prevent infections.
Meanwhile, research shows that up to 20% of all diagnoses are wrong, leading to harm to patients and unnecessary spending, Papier says.
With machine learning and artificial intelligence, there are a lot of people saying this is going to change medicine overnight and improve diagnosis. ... There is great promise.
Art PapierCEO and co-founder, VisualDx
"We've had technology around for quite a while where doctors could put in symptoms and get diagnostic possibilities and put in systems and lab possibilities and all kinds of data and be assisted with diagnosis," he continues.
"Now, with machine learning and artificial intelligence, there are a lot of people saying this is going to change medicine overnight and improve diagnosis. It's more complicated than that because medicine is complicated," Papier says. "But there is great promise."
