Learning Health Systems: A Multi-perspective Analysis

Abstract

Big time-gaps in the practical transformation of clinical knowledge into clinical practices, ever increasing healthcare costs, high rates of medical errors, healthcare institutions' obligations towards improving safety, clinical outcomes, and efficacy of care from one side and the rise of disruptive innovations such as genomics, wearables, machine learning and artificial intelligence in health systems, the adoption of EHRs and novel diagnostic tools, and the plethora of data from the other side has made the need for a new approach in managing the U.S healthcare systems an imperative. One of the ways in dealing with these changes is the establishment of Learning Health Systems (LHS) in which healthcare systems are aligned with continuous improvement and innovation from scientific, informatics, incentives, and culture aspects and knowledge about best practices is consistently captured, assimilated, and embedded within the system. However, despite the fact that the adoption of LHS is a multi-perspective one, there is not enough literature on the multidimensionality of this topic. This paper aims to identify and look into the different perspectives that have effects on the learning health system adoption in U.S by conducting PEST strategic analysis as an initial step towards a more multidimensional look into this topic.