How AI and the cloud are changing healthcare

From targeting chronic diseases and cancer to improving risk assessments, providers are tapping numerous cloud-based opportunities to deploy more precise, efficient, and impactful interventions at exactly the right moment in a patient’s care.

Jeff Rowe | May 08, 2018 10:11 am

While the cloud is enabling healthcare organizations to store and share ever-increasing amounts of data, it is also increasingly providing the foundational backdrop for a number of technological developments such as artificial intelligence (AI) and machine learning.

Writing recently at HealthITAnalytics, health tech writer Jennifer Bresnick observed that “AI offers a number of advantages over traditional analytics and clinical decision-making techniques.  Learning algorithms can become more precise and accurate as they interact with training data, allowing humans to gain unprecedented insights into diagnostics, care processes, treatment variability, and patient outcomes.”

Among the specific developments Bresnick describes are the capacity to unify “mind and machine” through brain-computer interfaces.

“Using computers to communicate is not a new idea by any means,” she explains, “but creating direct interfaces between technology and the human mind without the need for keyboards, mice, and monitors is a cutting-edge area of research that has significant applications for some patients.”

Another area where the cloud and AI are teaming up is efforts to expand access to care in underserved or developing regions. Shortages of trained healthcare providers, including ultrasound technicians and radiologists can significantly limit access to life-saving care in developing nations around the world, adding, “More radiologists work in the half-dozen hospitals lining the renowned Longwood Avenue in Boston than in all of West Africa.”

But AI could help mitigate the impacts of this severe deficit of qualified clinical staff by taking over some of the diagnostic duties typically allocated to humans.

“For example, AI imaging tools can screen chest x-rays for signs of tuberculosis, often achieving a level of accuracy comparable to humans.  This capability could be deployed through an app available to providers in low-resource areas, reducing the need for a trained diagnostic radiologist on site.”

Moreover, Bresnick observes, while EHRs have played an instrumental role in the healthcare industry’s journey towards digitalization, “the switch has brought myriad problems associated with cognitive overload, endless documentation, and user burnout.” To help rectify that, “EHR developers are now using artificial intelligence to create more intuitive interfaces and automate some of the routine processes that consume so much of a user’s time.”

In short, Bresnick argues that “by powering a new generation of tools and systems that make clinicians more aware of nuances, more efficient when delivering care, and more likely to get ahead of developing problems, AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.”