A recent VentureBeat article documented a fascinating use case of artificial intelligence (AI) in the healthcare sector. Researchers at the University of California at Berkeley have developed an AI system that can predict Alzheimer’s disease from brain scans.

In testing, the system “achieved 100 percent sensitivity at detecting Alzheimer’s an average of more than six years prior to the final diagnosis.” While the sample size was relatively small, this is an encouraging finding, and just one example to illustrate how AI and other technologies are poised to revolutionize all facets of healthcare.

Frost & Sullivan’s 2019 healthcare predictions point to AI, blockchain, and analytics as tech areas to watch in the year ahead. Looking specifically at the latter, Reenita Das, a partner and SVP of Healthcare and Life Sciences at the firm, wrote, “We predict that by the end of 2019, 50 percent of all healthcare companies will have resources dedicated to accessing, sharing, and analyzing real-world evidence for use across their organizations.”

The University of Chicago Medicine is one organization leading the charge in this area. The hospital implemented a solution using streaming analytics and an algorithm to predict when cardiac arrest is likely to happen. As a result, they have been able to significantly reduce the number of cardiac arrests, increase operating room efficiency, and reduce the rates of readmission. These and other benefits would not have been possible had the hospital not invested in a technology platform that eliminated data silos and enabled it to turn this information into specific actions to improve care.

The University of Iowa Hospitals and Clinics offers another great use case of how better access to data can improve patient health and deliver cost savings. The organization tapped predictive analytics to anticipate which patients are at risk for surgical site infections before they occur, leading to a 58 percent reduction in infections. When speaking about the project, Jose Maria Monestina, a senior application developer at the hospital, stressed the importance of unifying disparate systems and being able to easily share the resulting insights across departments and people.

As big data analytics adoption continues, expect to see more healthcare institutions develop initiatives similar to those described above. In fact, as Frost & Sullivan’s Das put it, “we foresee a high number of specialty-specific analytics solutions that will gain prominence among providers striving to investigate drug utilization, treatment variability, clinical trial eligibility, billing discrepancy, and self-care program attribution specific to major chronic conditions.”