Diagnostic Reading #24: Five “Must Read” Articles on HIT and Radiology
Reading Time: 3 minutes read
In the news: radiologists are stretched thin; roundtable gives advice for purchasing diagnostic imaging equipment.
This week’s articles in Diagnostic Reading include: Apple will open up its health records API; radiologists are stretched thin—and it’s affecting the next generation’s education; Mass General and Brigham and Women’s to apply deep learning to medical records and images; a roundtable discussion on how the ongoing shift from volume to value could radically change decisions about the purchase of diagnostic imaging equipment; and the future of personalized health.
Apple to open up health records API to developers and researchers – Apple NewsRoom
Apple announced that it will open up its Health Records application programming interface (API) to developers and researchers this autumn so that they can create an ecosystem of apps that use health record data to better manage medications, nutrition plans and diagnosed diseases.
Radiologists are stretched thin—and it’s affecting the next generation’s education – Radiology Business
Radiology education has made substantial progress since its debut in the medical sphere, but students and faculty alike continue to suffer from communication barriers, high burnout risks, and a lack of defined roles in the classroom, a group of administrators wrote in a compiled advice column for the JACR. The authors report the greatest challenges in a radiology learning environment stem from its faculty, who are expected to practice, innovate, and teach the next generation of radiologists simultaneously. With ACGME program requirements piling on the work, they said, staff are forced to juggle ever-increasing clinical workloads while also making time for scholarly activities and faculty development.
Artificial intelligence is beginning to reshape healthcare and life sciences with deep learning, a type of machine learning based on data representations rather than task-specific algorithms. Learning can be supervised, semi-supervised, or unsupervised. A single radiologist may have to look through thousands of images a day. Having systems that can help point out an abnormality in a stack of normal images, and then automatically measure that abnormality like a tumor or a dilated heart chamber, could be a huge productivity advantage for radiologists. Read the related article on how analytics can increase the value of enterprise imaging.
The ongoing shift from volume to value could radically change decisions about the purchase of diagnostic imaging equipment. To gain insight on this topic, Carestream reached out to leaders in the imaging community. MD Buyline analysts, AHRA’s president, UVA’s CMIO, and others gave their perspective on how to allocate and invest in technology to ensure that not only are the right resources in place today, but also for success in the future.
The future of personalized health is scientific wellness – Healthcare IT News
The convergence of personalized medicine with digital health and artificial intelligence, systems biology, social networks, big data analytics, and precision medicine is on the cusp of enabling an emerging field: scientific wellness. This new era will eclipse the disease industry that exists today.
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