Adding Intelligence to Medical Devices and Applications
One company at the forefront of change is GE Healthcare. In recent years, the company has embraced machine learning as a driver of better patient outcomes, with applications ranging from data mining platforms that draw on patient records to analyze quality of care to algorithms that predict possible post-discharge complications.
As part of its investment in machine learning, the healthcare technology company partnered with clinicians at the University of California, San Francisco to create a library of deep learning algorithms centered around improving traditional x-ray imaging technologies like ultrasounds and CT scans. By incorporating a variety of data sets—patient-reported data, sensor data and numerous other sources—into the scan process, the algorithms will be able to recognize the difference between normal and abnormal results. According to a recent survey, 82 percent of healthcare decision-makers say the use of data is already resulting in improved patient care, while 63 percent report lower readmission rates.
“The more intelligence we can put into medical devices and applications, the more we can increase the quality,” says Keith Bigelow, general manager of analytics at GE Healthcare. “It’s going to improve access, it’s going to improve efficiency and it’s going to reduce costs all at the same time.”
"The more intelligence we can put into medical devices and applications, the more we can increase the quality. It’s going to improve access, it’s going to improve efficiency and it’s going to reduce costs all at the same time."
Keith Bigelow
General Manager of Analytics
GE Healthcare
"The more intelligence we can put into medical devices and applications, the more we can increase the quality. It’s going to improve access, it’s going to improve efficiency and it’s going to reduce costs all at the same time."
Keith Bigelow
General Manager of Analytics
GE Healthcare
The goal of the innovation is to allow physicians to treat patients more quickly, not only cutting costs but also improving outcomes. Behind it, however, is GE Healthcare’s partnership with Amazon Web Services (AWS), which provides the ability to deploy machine learning solutions at scale through the Amazon SageMaker machine learning platform.
“We want to use the AWS platform to scale to as many algorithms as we can,” Bigelow says. “There’s a certain saying that I love: ‘Gravity is not just a good idea, it’s the law.’ And so the more we can leverage Amazon, through gravity—so that we can focus on a potentially life-saving use of machine learning—the better.”