Hospitals can feel pain, too, and computer scientist Karley Yoder is using artificial intelligence to treat it.
Yoder and her colleagues at GE Healthcare are working on apps that enable healthcare providers to analyze the reams of data their facilities generate, quickly diagnose problems and propose treatment. “Hospital operators know in their gut about problems, but they can’t quantify them,” Yoder says. “We give them the tools to identify and resolve those pain points.”
Yoder is spending this week in Chicago at the annual meeting of the Radiological Society of North America (RSNA), the world’s largest gathering for radiologists and other medical professionals. She and her team arrived with a new app that could make chest X-ray imaging more efficient.
X-rays represent more than half of all medical scans, and chest X-rays account for 50 percent of that volume.
Chest X-rays also leave a lot of room for error, Yoder says. She and her team have been searching for the root causes that can lead to bad scans and require the re-scanning of patients. “If you are a clinician taking an X-ray and your patient moves or you have a setting on your machine that inadvertently overexposes the image, you have to repeat the X-ray,” she says. “This exposes the patient to more radiation, slows down the patient flow and also adds extra paperwork because hospitals in the U.S. must report these instances to regulators.” These reports can take a day or more to compile, she says.
The app Yoder and her team developed takes care of the reporting automatically and also allows hospital managers to quickly spot problems. “It automatically pulls all of the information about rejected and repeated X-ray images into a one-page dashboard on the desktop that’s easy to review,” she says. “Administrators can drill in and see if there is a problem with a specific machine or a technician maybe needs more training. Once you quantify and identify the problem, you can quickly deploy a solution and move on.”
Yoder works in San Ramon, California, GE’s digital headquarters, but writing smart code is only one part of her job. The other is to make sure that healthcare practitioners won’t even notice the application running on their machines. “It’s like Netflix,” she says. “If you had to go to another application or open a new screen to get your movie recommendations, I doubt you’d use it.” She says that “there is always skepticism with new technology. We have to deliver it in a seamless way into the existing workflow.” She says that several GE Healthcare X-ray machines can already run the app and “the list is growing.”
The first iteration of the app will help hospitals reduce patient exposure to radiation, keep bureaucracy at bay, save time and make their radiology departments more efficient. But future iterations of the app or other similar advanced analytics technology may give technicians suggestions and maybe even help doctors with diagnoses. “When I think about deep learning in the healthcare space, I really think about it in three steps,” Yoder says. “We need to build the deep learning model and make sure it does what we want it to do. Next, we need to turn it into a product and deploy it. The third part is where the AI works and it gets really exciting because it allows me to work with the data and refine the model.”
Yoder says that one day artificial intelligence technology could help doctors send patients to the right specialists faster. It could also help them prioritize patients. “The machine can send them an alert: ‘Hey, take a look at this,’ ” she says. “I think that we are going to start seeing systems like this in many places in the healthcare field.”
And beyond, as well. Yoder’s colleagues in San Ramon work on smart software for aviation, energy and other industries where GE is active. She recently discussed her work with an engineer who suggested a possible use of the app in the oil-and-gas field, which is using industrial-grade X-ray machines to image parts for pipelines and other components. “We are very early in these conversations, but it’s the same technology,” she says. “But if you can scan hundreds of images and use an app to identify conditions like pneumothorax, you can probably use it to find a problem with your pipeline.”