Advancing Decision Support Systems with Data, Informatics, and Learning Health Systems in a Post-Meaningful Use Era

The HITECH Act and Meaningful Use (MU) successfully drove widespread EHR adoption but largely missed the strategic goal of healthcare transformation. While EHRs are now ubiquitous, few institutions have transitioned to a true Learning Health System (LHS). To support achieving transformative impact, this article proposes an extension of a previously-described quality improvement life cycle model, aligning it with modern informatics and artificial intelligence (AI) capabilities. We detail how the stages of DMAIC (Define, Measure, Analyze, Improve, Control) provide the necessary structured framework for advancing decision support within a post-MU environment. We argue that by integrating AI-driven tools into each step of the DMAIC cycle, healthcare organizations can effectively leverage their vast stores of electronic clinical data. This structured approach offers a pathway to establishing agile, data-driven learning health systems necessary to fulfill the latent promise of health information technology.