Digital health is associated with the use and development of digital technologies to improve health. Use, development, and design of the according technologies have in recent years led to a considerable shift. Specifically, practices affecting an individual's health have been shifted from inside hospitals into wider digital health ecosystems where providers, patients, their loved ones, and laypersons interact to jointly shape how care is provided. IS research has greatly advanced in terms of better understanding the technological foundations of this shift such as artificial intelligence (AI) applications, sensor-based technologies, and smartphone apps. However, at least two major trajectories of research arise from this shift and its underlying technologies.
First, creating value in digital health ecosystems demands to take into account diverse kinds of value to be potentially created through these technologies. Financial value is an important kind of value to be created in digital health ecosystems; however, it is by far not the sole kind of value that matters in these settings. It is important to understand which kinds of value technology helps to create as well as how, why, and when it does so. Second, creating value is not per se synonymous with distributing it so that an important line of inquiry is about how to distribute value among ecosystem participants and over time.
We are asking for papers speaking to these topics and see two broad ways in which submissions to our special issue could do so. The first is cumulative and much in line with how research in IS and adjacent fields such as computer science, management, and medicine is conventionally done. It resides in furthering our understandings and toolkits for creating value in digital health ecosystems. However, the more data are available the higher is arguably the potential for misuse, especially when genomic and bio data are in question. This is why research about creating value from data logically calls for research about how to distribute value among ecosystems participants.
The second way to address the abovementioned research trajectories is consistent with promoting contrarian studies. We see much promise of contrarian studies investigating the creation and distribution of value in digital health ecosystems. Research about digital health is replete with assumptions touching on the outcomes that large volumes of data are likely to generate. This stands in stark contrast to the fact that on the ground floor where most clinicians work the data are hardly ever available in the format, quality or volume needed to even remotely live up to these expectations. It is, therefore, paramount to offer fundamentally new ways of thinking about creating and distributing value in digital health ecosystems.