Implementing Big Data Analytic Platform in Healthcare The Israeli experience

Abstract

Abstract Background: Medical big-data processing enables analysis of complex multifactorial clinical situations, assessing medical decisions alongside hospital strategic planning and business goals. However, accessing this data is challenging due to legal-ethical, technical and methodological barriers. It also requires the cooperation of multiple partners. Other health systems also struggle to balance scientific innovation and regulations. Purpose: to establish a practical functional integrative model to overcome these substantial barriers. Methods: An anonymous big data cloud based data warehouse was created de novo using artificial intelligence algorithm. Major barriers to data access and anonymization were identified and targeted solutions were constructed. Results: An operating model provided secured anonymous data to ongoing four internal research projects in a single tertiary state medical center. Additional four state medical centers joined the program. Conclusions: our experience demonstrates the feasibility of creating an integrated functional dynamic medical big data, accessible by multiple users in a virtual cloud. Further studies will determine its cost-effectiveness and potential value for medical research and biomedical industry . A step by step implementation, involving all relevant stakeholders enables an acceptable national model despite local barriers.

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