Software as a Service (SaaS) is coming to healthcare, in part because cloud computing will help healthcare organizations more quickly take advantage of the latest trends in computing, from big data to artificial intelligence (AI).

“SaaS really adds value by giving people that constant access to the latest technologies, concepts, and advances,” said Todd Donzelli, SaaS Transformation Leader at GE Healthcare. Strategic benefits, rather than cost savings, are the main driver for cloud adoption in healthcare, he said, adding that cloud subscription models do offer predictable spending, “which is really useful for any organization from a budgeting perspective.” 

Indeed, many hospitals and private practices are migrating databases, applications, and services (such as disaster recovery) to the cloud. Two-thirds of IT leaders from health systems, hospitals, and other large healthcare organizations reported that they currently use the cloud or cloud services at their facilities, according to data published by HIMSS Analytics.^[1] Nearly 9 in 10 of these respondents (88 percent) said their organizations are using a SaaS model, up from 67% in a HIMSS cloud survey from 2014.

Easing data sharing

Hospitals and health systems grapple with data access and use today. Seven out of 10 have significant (20%) or moderate (49%) challenges integrating/connecting data from multiple or siloed data sources, according to an April 2019 survey conducted by HIMSS Media on behalf of GE Healthcare.^[2] On the other hand, a majority of the 100 respondents said they saw an immediate opportunity to bring AI/ML into the clinical workflow (getting information to the right people at the right time) and into the management of chronic health conditions, and 4 in 10 said they are making AI and ML a focus of greater strategic activity in the next 12 months.²

By simplifying data sharing, the cloud should help accelerate the use of radiology AI in routine clinical practice. Creating methods to encourage data sharing was one of four priorities sketched out in a report published online May 27 in the Journal of the American College of Radiology.^[3] The report, written by a team of authors led by Dr. Bibb Allen Jr. of the American College of Radiology (ACR) Data Science Institute, also identified as priorities structured use cases, validation and monitoring tools, and new standards and data elements.

Data sharing to support the training and testing of AI algorithms would promote generalizability of AI algorithms to widespread clinical practice and mitigate unintended bias, according to the authors.

SaaS will be a benefit to healthcare vendors and practitioners, said Ulf Schoo, VP of Product Management for GE Healthcare. For vendors, it enables efficient software delivery, self-service services, and the ability to expand quickly into new markets. “And customers want on-demand, anytime, anywhere services like they’ve come to expect with platforms like Netflix,” Schoo said.

Inherently centralized, SaaS also “avoids the data silos in healthcare storage today,” Schoo added. And this will be a path building products that “aggregate, correlate, and analyze,” which should be a boon to individual patients as well as population health studies, Schoo said.

Acknowledging that some healthcare data sharing and access is prevented today because of regulatory frameworks, Schoo said it was nevertheless important for vendors like GE Healthcare to be ready with the technical infrastructure. “So that, in the future when legal frameworks change, you’ll be able to make it available quickly,” he said.

SaaS could also address the resource shortage for both data scientists and clinical specialists. Among the challenges measured in a HIMSS survey earlier this year, limited budgets and difficulty integrating data from across multiple and siloed systems are cited as significant or moderate challenges for 7 out of 10 hospitals and health systems today.^[4]

Not surprisingly, those shortages hit smaller hospitals (those with fewer than 500 beds) more acutely than their larger peers. This difference was reflected both in terms of budget challenges to invest in needed technology resources (80% vs. 63%) and difficulty integrating/connecting data from multiple or siloed data sources (73% vs. 65%).⁴

For GE Healthcare’s Donzelli, the sea change in healthcare will be subscription-type cloud models, giving access to the latest software and services, including AI models.

“When the imaging devices themselves become commoditized, it is the technology around those devices that becomes the real value-add for clinicians and patients,” he said. That technology will range from booking appointments to the scans themselves, to the post-scan diagnosis and data management.

Referring to radiology’s transition from a device-heavy industry into a technology-heavy industry, Donzelli said, “This is our Kodak moment.”