Personalized Big Data Access: Value for Medical Staff

Abstract

Healthcare systems produce big data with latent capabilities for healthcare providers. Big data is a strategic resource and requires the appropriate infrastructure for data entry, systematic analysis, and visualizations for decision makers. Attempts to build a big data infrastructure raise various challenges related to availability, accessibility, reliability, and quality while considering information privacy and security. These challenges are significant and can disrupt the ability to realize data’s hidden potential. A variety of technological tools are available to medical staff members for big data use in healthcare. However, access to the data alone does not guarantee the appropriate use of these tools and still requires understanding the needs of end users to ensure success. In recent decades, several models have been developed to evaluate the implementation of new information technology and the adoption of technology by users. The current paper focuses on this value and related challenges: how to turn organizational data into meaningful knowledge by introducing a new implementation model in big data in healthcare. The model is an integrated one, presenting practical aspects, timeline aspects related to the life of the project, personalization in access to data, reference to information providers, and technological solutions. The project uses an organizational architecture tool to describe the implementation model and generate outcomes. The model will be based on 15 clinical and managerial use cases. Outcomes will be described by strategic objectives in the model and will be presented in the ArchiMate® language.