Health systems can leverage the predictive potential of machine learning to improve outcomes, lower costs, and save lives. Machine learning, however, doesn’t inherently produce insights that are actionable in the clinical setting, and frontline clinicians need information that’s accessible and meaningful at the point of care. Thoughtfully designed visualizations of machine learning insights are a powerful way to give clinical users the information they need, when and how they need it, to support informed decision making.
A design framework for machine learning visualizations addresses three key questions about who will use the decision-support insights and how:
People: who are the targeted users?
Context: in what context or environment do they work?
Activities: what activities do they perform?