Over the last decades there have been increased in the application of information systems (IS) in the form of digital health technologies to enhance access to healthcare services, improve healthcare quality and efficiency of healthcare systems. Evidence on the efficiency of digital health technologies in clinical settings to enable the adoption and scaling up of such technologies is buried in ever increasing and complex extant literature including grey due to lack of the application of robust evidence-based research in IS. By and large, in the medical and health domain, decision-making is based on evidence-based research. In this study, we advocate for the increased use of evidence-based research using systematic literature review (SLR) as reference point in IS interventions especially in healthcare settings as digital health interventions increase. We believe the increased use/application of evidence-based research in the domain of IS will be key to identifying and scaling up relevant digital health technologies, and one step forward from “potential” as they are known for. We demonstrate this by liking IS research to medical research in various aspects. We also provide a step-by-step guide to conducting systematic review in IS including strategies on how to harness evidence through strategic use of the contentious grey literature.

The health industry is undergoing a critical transformation to become data-driven to catch up with other service industries. To achieve this, the industry would need to transition from its current conventional digital health models, which are impacted by several factors ranging from data quality and availability, technology advancement/trend, enabling ecosystem, public-private partnership, and care consumer participation to data skills development and change management. It would need to create a learning health system (LHS) when it integrates and inculcates knowledge-generating capabilities in its operations to take advantage of big data to identify healthcare service quality gaps and evaluate interventions for progressive improvement. But first, it has to continue to explore and come up with novel data-generating innovations, the use of data by healthcare consumers and providers, and reliable data regulation to ensure privacy and security as factors that influence data-driven healthcare design/models.