Providers go "all in" to meet changing consumer demands

The 21st century health care consumer is already comfortable with cloud and mobile technology, so they're making decisions based upon how well practices and hospitals are using these digital tools to meet their needs. However, providers have been typically slow to change. A recent survey by Ambra Health of over 1,000 U.S. adults showed that it still takes 44 percent of patients over a day to have imaging sent to a physician, and most of that transfer is still occurring through CDs. Facilities must understand imaging consumer preferences, which frequently include items like an image-enabled patient portal, "family share" plans, mobile access and more. Through widespread cloud technology adoption facilities can empower patients with control over their own image data by eliminating CDs, image-enabling patient portals and providing easy, 24/7 access to patients.

Data security will move from protection to prevention

Data security continues to be a major factor as health care systems move to centralized cloud storage facilities. With security technology, talent, and awareness improving across all industries, security tactics will shift with the help of AI and machine learning from reactive protective measures to proactive preventative ones. Data security teams, with the help of machine learning and AI, will have an increasingly important role for predicting security infringements before they happen. Health systems and practices will need to continue to invest heavily in people with IT security skill sets and products that leverage machine learning to get ahead of global IT security challenges.

An uneven rise of AI for workflow automation

The latest machine learning, deep learning, and workflow automation technology can accelerate interpretation, improve accuracy, and reduce repetition for radiologists and other specialties. During the annual RSNA conference in November, AI was by far the most talked-about trend for 2018 – on both ends of the spectrum. Many practitioners are excited about its potential to transform everyday workflows, while others are skeptical that AI's moment has arrived. Your point of view may largely depend on your practice or health system's data infrastructure.

AI's potential rests in its ability to tap large data sets and run algorithms through stored patient data. There are certainly many potential use cases for AI workflow improvements in radiology. One example is to use algorithms to automatically align current and prior exams for instant comparison of multiple studies. Machine learning, when combined with artificial intelligence, can even further improve processes by scanning imaging. The rise of AI applied to workflows will be uneven in 2018, with health systems that have the infrastructure in place already off to the races.

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