June 26, Rizwan Malik
Instead of revisiting the well-trodden road of what Artificial Intelligence, deep learning, machine learning, or natural language processing might be, the data-science that underpins them, or what products and algorithms are currently available or pending, let’s consider how AI can impact the patient journey in radiology. From pre-imaging to image acquisition, reporting and post-reporting, AI could result in faster and more efficient patient-centric imaging.
June 18, Hans Duvefelt
In spite of all the talk about team based care, medical providers today are terribly isolated. We are all collaborating with other staff categories, but not so much with each other. Instead of eating at our desks, holing up to stare at our smartphones, or giving up our lunches for structured meetings, we could find ways to interact with each other at work—eat lunch together and talk about tough cases, new things we’d like to try, and challenges we face as modern medical providers.
June 14, Adrian Gropper and Deborah C. Peel
The draft rules for interoperability are over a thousand pages. Most of the complexity stems from a design that avoids direct patient direction and transparency the way we expect banking and other automated services. This approach fragments the patient and physician experience and poses privacy and security risks that may never be solved. At the end of the day, we must treat patients and physicians, not the data brokers, as the real stakeholders.