A Schematic View of the Application of Big Data Analytics in Healthcare Crime Investigation - Yange, Terungwa Simon; Soriyan, Hettie Abimbola; Oluoha, O (Journal of Health Informatics in Africa, 2017)
Abstract Background and Purpose: One major challenge encountered during crime investigation via automated systems is the inability of conventional data analysis techniques to adequately handle the enormous data that are made available during the investigation. Existing crime investigation frameworks are built on orthodox data analysis techniques which cannot sufficiently manned the unprecedented size and variety of data available today, not to mention the significantly more anticipated data in the near future. This has affected the healthcare industry where data is predominantly multi-structured and is growing at a considerably faster rate. Methods: To address this, a big data analytics model based on deep learning was designed in this research using enterprise application diagrams. Results: This model is intended to be implemented using Apache Hadoop a big data implementation framework. When implemented, the model will create a platform that will handle a phenomenon that is affecting millions of people all over the world. Conclusions: This is the first of its kind to use big data analytics techniques in healthcare crime investigation in Nigeria which provided security intelligence by shortening the time of correlating and deriving evidence from large volume of data for healthcare crime investigation purposes. Finally, this research also enabled the healthcare systems to systematically use big data analytics to identify inefficiencies and best practices that improve care delivery and reduce costs. Keywords: Crime, Hadoop, Deep Learning, Investigation Data Analytics, Health Insurance