Abstract
Health analytics is the analysis of health data to uncover patterns, trends, and insights that can inform decision-making and drive healthcare improvements. While health analytics has considerable potential to improve healthcare and health outcomes, converting the potential value of health analytics to realized value is nontrivial. Further, the health analytics research context sits at the nexus of business, technology, computation, and health, making contributing to theory complex. To highlight opportunities in this space for theory contribution, this editorial and special issue offer guidance on and examples of how health analytics research can contribute to theory. In this editorial, we introduce four papers and suggest a number of research opportunities. Opportunities proposed in this editorial include: leveraging the health analytics context to open the black box of problemistic search, using emerging causal machine learning approaches to systematically generate new hypotheses from health-based data, addressing equity by improving the fairness of resource allocation decision-making in health processes, identifying patterns of human-AI collaboration in healthcare processes, and applying methods such as policy learning to health treatment assignment optimization.
Recommended Citation
Baird, Aaron; Xia, Yusen; and Kohli, Rajiv
(2025)
"Health Analytics and IS Theorizing,"
Journal of the Association for Information Systems, 26(3), 575-588.
DOI: 10.17705/1jais.00945
Available at:
https://aisel.aisnet.org/jais/vol26/iss3/10
DOI
10.17705/1jais.00945
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