Machine learning-enabled healthcare information systems in view of Industrial Information Integration Engineering

Recent studies on Machine learning (ML) and its industrial applications report that ML-enabled systems may be at a high risk of failure or they can easily fall short of business objectives. Cutting-edge developments in this field have increased complexity and also brought new challenges for enterprise information integration. This situation can even get worse when considering the vital importance of ML-enabled healthcare information systems (HEIS). Therefore, the main argument of this paper is that we need to adopt the principles of Industrial Information Integration Engineering (IIIE) for the design, development, and deployment processes of ML-enabled systems. A mixed research paradigm is adopted, and therefore, this study is conducted by following the guidelines and principles of Action Research, Design Science Research, and IIIE. The contributions of this study are two-fold: (a) to draw researchers' and practitioners' attention to the integration problems of ML-enabled systems and discuss them in view of IIIE, and (b) to propose an enterprise integration architecture for ML-enabled HEIS of a uni-versity hospital, which is designed and developed by following the guidelines and principles of IIIE.