The objective of this special issue is to showcase how health analytics can be used to enhance theorizing in the field of IS. In addition to its contribution to deep analysis, the power of health analytics research lies in theory development that leverages the unique context of health care. Specifically, the special issue aims to foster research that addresses the question: How can the application and design of analytics methods identify insights from health data and extend IS theory?
This special issue places analytics at the forefront of IS research so that health care researchers can make a theoretical contribution to IS research. Through this special issue, we hope to provide an opportunity for emerging and innovative health analytics research to be published that will showcase how new forms and combinations of theoretical reasoning, methods, and data can contribute to theory building. Our hope is that this special issue will contribute to deeper descriptions, explanations, and predictions of emerging health phenomena relevant to IS scholars as well as demonstrate to clinicians and patients opportunities that will enrich health care management and delivery. We welcome research at the intersection of traditional and emerging approaches, exploratory research, phenomenon-based research, novel methods, and data from any relevant source (with IRB approval, as needed). While we are open to discoveries of all kinds, we expect application of rigorous methods, presentation of persuasive reasoning, and inclusion of strong evidence.
Health analytics can be generally described as generating insights from health data through analysis. Significant and impactful work in health analytics is emerging in the IS literature, but there are also significant opportunities to leverage health analytics research to contribute to theorizing in IS. For instance, while interesting findings within IS have been presented in the context of health analytics use in hospitals and clinical diagnostics or care, many health analytics research contexts have yet to be exploited in IS research. The diversity of data and contexts within which to conduct health analytics research is substantial and such diversity is currently underrepresented in IS journals. We propose that health analytics research can advance beyond presentation of context specific models and methods. We see considerable promise in applying such innovative approaches to the enhancement of IS theory, particularly in explaining IS-enabled mechanisms, through research conducted in the health analytics context.