Paige McDonald and colleagues detail key domains, tools, and actions required to enact learning health systems for continuous intelligent improvement in healthcare
Despite an increased focus on quality improvement in healthcare over the past 50 years, many of the same problems persist. This disconnect has been described as the 60-30-10 challenge: only 60% of care, on average, aligns with evidence or consensus based guidelines; another 30% of care is waste or of low value; and 10% of patients experience adverse events.1 These statistics have persisted for over three decades.2 Even with technology providing increasing volumes of data and more sophisticated analytical techniques such as machine learning and artificial intelligence, progress continues to be painfully slow.
Learning health systems provide a pathway towards continuous improvement and innovation in healthcare through the routine collection, analysis, and more timely use of data. The US Institute of Medicine first proposed the concept as systems in which “science, informatics, incentives, and culture are aligned for continuous improvement and innovation” in response to increasing complexity in healthcare and a need to improve the quality of care while reducing inefficiencies and costs.3 Yet, a lack of practical guidance on how to enact such systems and a dearth of evidence indicating return on investment has led to justifiable scepticism about their achievability and prospective value.4
Despite this, properly implemented learning health systems locally and globally still have potential to improve healthcare delivery. While efforts have focused on the use of data, evidence based practice, and quality improvement, healthcare systems have not integrated these approaches for continuous learning. In this analysis we seek to clarify the concept of learning systems and provide examples of their application and outcomes.
Learning health systems have been defined as health systems “in which internal data and experience are systematically …