Health systems are rapidly moving towards digitally-enabled systems as the industry shifts from volume- to value-based reimbursement, and in the process they accumulate complex data sets. Making sense of this data is crucial to help manage costs, improve patient outcomes, and explore future business models and innovations. Intelligent computing capabilities have emerged as a promising tool to derive more value from data through improved predictions and process automation.
Machine learning, in particular, is becoming a mainstream part of mature health system analytics programs. As health care organizations continue to build up their analytics capabilities in support of core health system challenges such as population health, value-based care transformation, and care variation reduction, machine learning is poised to become even more valuable.
This research report provides an outline of the intelligent computing ecosystem, explains the role of machine learning in health care, and provides case studies of how UNC Health Care and Intermountain Healthcare are leveraging machine learning.