Introduction to the Special Issue on Design and Data Science Research in Healthcare

Abstract

For many decades, ‘design science’ was used to depict the process around the systematic formation of artifacts. In information systems, the term is used more broadly to describe systematic approaches to creating an expansive set of diverse artifacts, ranging from knowledge frameworks to full-fledged information systems. Design science in information systems denotes research that focuses on the creation of new technology, knowledge about technology, and the process of creation. ‘Data science’ refers to an interdisciplinary field that focuses on data and its collection, preparation, and integration. Although different from ‘design science,’ ‘data science’ also has seen increasing use in the information systems (IS) literature. The growing availability of high-quality software libraries and technology to reuse existing code has most likely contributed to this increase. Regardless, data science research plays an essential role in the increase in design science research. Hevner et al. [2004] portray design science in a framework comprised of the environment, information systems research, and an application domain. They suggest that design science research addresses important unsolved problems in unique or innovative ways or that it solves problems in more effective or efficient ways. Similarly, the Design Science Research knowledge contribution framework later developed by Gregor and Hevner [2013] proposes three types of research contributions: developing new solutions for known problems, extending known solutions to new problems, and inventing new solutions for new problems. In contrast to other computing fields, the IS field has historically emphasized using kernel theories to invent, adjust, and improve artifacts. However, notable contributions can also be made without reliance on kernel theories in the intersection of data science and design science. For example, no comprehensive theories explain why artificial neural networks (ANNs) work as well as they do. And yet, ANNs serve as a cornerstone technology in most classification projects ranging from tumor identification in medicine to recognizing handwritten checks or recommendations in e-commerce. Even when theories exist, they may be irrelevant to the artifact design. For example,