AuntMinnie.com rolled out the virtual red carpet on October 25, announcing the winners of the 2017 Minnies. The awards, which celebrate the best ideas and brightest minds in radiology, recognized numerous contributions that are advancing a future shaped by augmented radiology. IBM Watson Health is proud to have been recognized for our work in this field, taking home the award for Best New Radiology Software for IBM Watson Imaging Clinical Review.
The reading room reality
Radiologists spend just one third of their time on image interpretation
. For the majority of the day they are either supervising studies, performing image-guided procedures, teaching, in meetings, consulting with physicians, or directly caring for patients. Their work day is dynamic and full of interruptions, making effectiveness in the reading room an ongoing challenge.
Being efficient and productive in the dark room becomes increasingly important when radiologists encounter rare and complicated cases that require knowledge of patient history to complement the evaluation of medical images. This often means searching for patient information by manually sifting through multiple, extensive, disorganized and disconnected EMR records. Moreover, the situation is exacerbated by the fact that approximately 80% of patient data is unstructured
(e.g. notes and reports). Reading and understanding this data in order to uncover clinical meaning relevant to the imaging study at hand, disrupts radiologists’ workflow and takes up valuable time. In an age of increasing demands on reading physicians, this is a burden they can hardly afford.
Yesterday, IBM announced the creation of the Watson Health medical imaging collaborative. This global initiative unites 16 leading health systems, academic medical centers, ambulatory radiology providers and imaging technology companies – including Merge Healthcare – with the aim of assisting physicians in the diagnosis and treatment of conditions such as breast cancer, diabetes and heart disease. The members’ combined efforts will help Watson to extract insights from previously ‘invisible’ unstructured imaging data which will be combined with a variety of data from other sources. The end result may help physicians make personalized care decisions for specific patients while building a body of knowledge to benefit broader patient populations.