Electronic Health Reporter

Guest post by Steve Tolle, chief strategy officer, Merge Healthcare, an IBM Company.

The volume of health-related data available to physicians and other healthcare providers from disparate sources is staggering and continues to grow. In fact, a 2014 University of Iowa, Carver College of Medicine report projects that the availability of medical data will double every 73 days by 2020. Such data overload can make it difficult for clinicians to keep up with best practices and innovations.

Perhaps because imaging is so pervasive in healthcare, the medical imaging field has turned to data analytics and cognitive computing to help clinicians use large volumes of data in a meaningful way. These decision-support tools help them manage data to improve patient care and deliver value to referring physicians and payers.

At RSNA15, the crowds packed presentations on data analytics and cognitive computing and flocked to vendor exhibits featuring these decision-support tools — indicators of their expanding role in healthcare. In years past, exhibit space was primarily devoted to showcasing new imaging modalities.

Interest in analytics is growing rapidly as the U.S. health system transitions from volume- to value-based payment models — models that challenge physicians involved in medical imaging to demonstrate value. Physicians are under pressure to deliver educated, accurate, useful and efficient interpretations even as imaging studies become increasingly large in size and complex in scope. And these physicians are expected to communicate this information quickly and in a user-friendly manner. As a result, clinicians are turning to analytics-based solutions to boost efficiency and enhance the quality of their service to help them deliver the value demanded by payers, referring physicians and patients.

Data analytics capabilities have quickly moved beyond dashboards that track imaging volume and report turnaround times. Advanced analytics are helping to streamline imaging workflows and manage prior authorizations, in addition to reducing average scan times, variability in studies and patient waiting time. It is clear that we have entered a new era in medical imaging. A number of small and large companies are moving the field forward with cloud-based data analytics and cognitive computing initiatives.

At RSNA15, attendees saw a vision for the future of medical imaging that incorporates cognitive computing. The exhibit demonstrated how a physician could gain assistance from machine learning algorithms that simulate the human thought process to assess structured and unstructured data from disparate sources — including medical imaging at scale. A reasoning engine connected to a vast image library and patient health records was able to analyze large volumes of data and find correlations that humans may have missed or never considered.  In seconds, it derived new insights and arrived at a potential differential diagnosis for consideration by the physician. The goal of this futuristic model is to augment the physician’s own experience and insights and improve diagnostic accuracy.

We have only just scratched the surface of the potential for cognitive computing and advanced analytics to advance and improve healthcare. With about 80 percent of available healthcare data currently going unused and the volume of new data increasing exponentially, there is no doubt that these decision-support tools will take center stage in 2016 and beyond to address our nation’s complex healthcare issues.