As we amass more data, we have an opportunity to analyze a pseudo-population to better understand differences in health across groups. For example, comorbidity is a medical condition when a patient develops more than one disease simultaneously. The way patients belonging to different population groups develop comorbidities can have a major impact on their health outcomes. Therefore, there is a strong need to know these differences in comorbidities across population groups. In this study, we apply the grounded theory methodology lens to compare the comorbidities across population groups. First, we create a comprehensive network for each population group and then compare their structural properties. This leads to developing multiple research questions that need to be explored in the future research. The interesting findings and theoretical implications are discussed.
Kalgotra, Pankush and Sharda, Ramesh, "Health Analytics Lead to More Questions: A Comorbidity Lens Approach" (2016). Proceedings of the 2016 Pre-ICIS SIGDSA/IFIP WG8.3 Symposium: Innovations in Data Analytics. 18.