ABSTRACT
In today's high-cost healthcare environment, high value and strong clinical outcomes are paramount. An area of growing concern is around unplanned readmission risk. Patients with diabetes and other comorbidities are typically overrepresented in this regard. This chapter relies on action design research for designing and implementing Information technology artefacts to understand the risk of 30-day unplanned readmission of comorbid patients of diabetes from diverse cultural backgrounds to create a digital tool that will facilitate effective management of diabetes. Data-driven decision support, which refers to the reliance on Knowledge discovery and data mining via statistical, mathematical, and machine learning algorithms for abstractions has enhanced clinical and non-clinical decisions in many organizations and is integral in the presented approach.
