Gauging the Gaps for Decision Support - Data integration in the Hospital Information Systems with Machine Learning

It has been trendy to embedded machine learning techniques in enhancing decision supports in the organisations. The essential expectation for getting accurate prediction and estimation via such tool is the integrated systems and data quality - accuracy, completeness, consistency, timeliness, validity, and uniqueness. As suggested by the literature, however, hospital information systems are fragmented, and various departments implement various expert systems from different vendors due to the nature of medical complexity. Therefore, this paper proposes a conceptual framework that explains how data could be integrated from the separated systems for clinical decision support with a context of emergency department and how machine learning systems can be placed in the architecture of the completed hospital information systems.