By Dr. Taha Kass-Hout, MD, MS, Chief Technology Officer of GE HealthCare

From the Victorian doctor with a stethoscope to the modern oncologist with a CT scanner, healthcare professionals have always relied on the best data available to guide their decisions. As technology has evolved, medical data has improved in step — giving us a clearer picture of what is going on inside patients’ bodies and helping to model the risks and benefits of potential treatments.

Nowadays, however, clinicians can’t harness the full power of that data, due to its sheer volume — more than 5.5 billion imaging procedures are performed every year, for example — and fragmented origin, and mostly lack of any structure in order to search or analyze. A cancer patient might visit several hospital departments — oncology, genetics, pathology, radiology — each of which collects information about the patient using specialized hardware from different vendors. The resulting data is complex in being multimodal (image, text, trace, values, etc.), and disparate, so it’s difficult to curate or analyze in a way that offers immediate insights to clinicians.

Of course, patients don’t conceive of diseases along the lines that define medical specialties; an illness like cancer can take a toll on every aspect of their lives. To offer personalized, precision medicine, we must look at care the same way, creating a cohesive mosaic of our findings for each patient. That goal lies at the heart of GE HealthCare’s digital strategy. By aggregating and analyzing information quickly and accurately, digital technology has the potential to solve some of the biggest problems in healthcare today. In addition to integrating heterogenous data, it can also eliminate inefficiency in workflows, ease staffing shortages, and prevent employee burnout.

For nearly a decade, GE HealthCare has been a leader in the medical field’s shift to cloud computing and implementing advanced machine learning applications in the clinical workflow. Cloud and advances in machine learning are at the heart of our digital strategy and offerings. In a cloud system, computing services like databases, servers and software are delivered on demand over the internet. Cloud solutions can help our healthcare customers reduce their total cost of ownership (TCO) of maintaining and upgrading on-site hardware, ensuring security and privacy of the information, and they process data many times faster. It can be more cost-effective for providers to run advanced cloud-based digital tools, enabled by machine learning, that can integrate and analyze vast amounts of input from different sources. We are continuously expanding our constellation of cloud-based products to help providers overcome their toughest challenges.

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Just as digital technology has allowed us to stay connected in our daily lives — to each other and to the information we need — cloud technology can bring connectivity to healthcare. Our team is driving the development of GE HealthCare’s D3 framework (the three Ds stand for device, disease state and digital), which will assimilate input from imaging and monitoring devices, smart devices like wearable monitors and sensors, and even robotic assistants in surgical units. When this information is stored on the cloud, each stakeholder across the entire workflow, from physicians and clinicians to radiologists and technicians, can securely access it from anywhere, through an internet-connected device. Analytics will integrate this data to create a comprehensive view of treatment, so clinicians can collaborate more efficiently and ensure each patient receives the appropriate intervention at the right time.

Artificial intelligence (AI) is also central to our digital strategy. Designed to find subtle patterns within reams of data, AI tools can help clinicians make decisions with the most complete, relevant and up-to-date information. For example, GE HealthCare’s AIR Recon DL is a deep-learning-based reconstruction algorithm that enables radiologists to achieve pin-sharp images quicker, while improving signal-to-noise ratio (SNR) which means scan times are cut by up to 50%, smoothing workflow and enhancing the patient experience. GE HealthCare Critical Care Suite, a collection of AI algorithms embedded on a mobile X-ray device, automatically analyzes images for critical findings like pneumothorax and enables radiologist worklist prioritization. A recent review at University Hospital Cleveland showed that while using Critical Care Suite, there was a 78% decrease in reporting time — from 3 hours and 22 minutes down to 44 minutes — for urgent exams positive for pneumothorax. When embedded in existing workflows, AI is a powerful tool to organize data, detect patterns and identify aberrations early, and ultimately synthesize so clinicians don’t have to comb through thousands of images.

Going forward, AI is poised to play an even larger role in healthcare. We believe that with its power to derive population-scale insights from patient data, machine learning will eventually transform the healthcare system. In the nearer term, AI may alleviate one of medicine’s most pressing problems. The industry faces a worsening staff shortage — the World Health Organization predicts a shortfall of 10 million healthcare workers across the globe in 2030. To make matters worse, clinicians are already struggling with burnout from stress and cognitive overload.

All these advancements can simplify and automate processes to help improve efficiency, reduce waste and close gaps in care, and address physician burnout by automating many routine tasks — allowing them to focus on what’s most important: the patient.

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