Exploring Data-Driven Innovation in the Clinical Domain

Data-driven innovation is a central objective in the field of Medical Data Science (MDS), aiming to leverage both clinical and non-clinical data to generate clinical value. Despite the rapid advancement of analytical methodologies and tools, including sophisticated AI models, the adoption of data-powered analytical software still lags significantly. Previous research has explored the challenges hindering practical adoption, but our study goes further by offering a comprehensive view of the data-driven innovation process in the clinical domain.

Theoretical Framework

Our research integrates best practices from diverse domains and industries with the specific challenges inherent to the clinical sphere. We present a theoretical framework to structure data-driven innovation in the clinical domain. This framework encompasses various forms of data-driven innovation in clinical settings, delineates the primary and secondary stages of the cyclic process, outlines their interdependencies with concurrent innovation cycles, and identifies primary challenges unique to each phase.

Contributions

Through this work, we establish the groundwork for categorizing data-driven innovations in the clinical domain, assessing their maturity, and pinpointing key challenges. It's important to note that our focus in this research is on providing a comprehensive overview, and we refrain from providing detailed strategies for addressing these challenges. In forthcoming work, we plan to delve into technological approaches for surmounting these hurdles.