Big Data in Healthcare: Acquisition, Management, and Visualization Using System Dynamics

Integrating big data, electronic health records (EHR), and health information systems (HIS) has yielded trans-formative benefits in healthcare. However, it has also introduced critical risk factors. These risks encompass data privacy and security concerns, necessitating robust encryption techniques and adherence to regulations like the Health Insurance Portability and Accountability Act (HIPAA). Additionally, data inconsistencies stemming from human behavior and decision-making can compromise data quality, urging strict data quality management and governance. Data fragmentation, primarily due to disparate data storage systems and limited interoperability, challenges comprehensive data integration. The accuracy of data in extensive datasets poses another challenge, with adaptations of quality management techniques required. Re-identification risk, wherein supposedly anonymous data can be traced back to individuals, underscores privacy breaches. Lastly, the evolving landscape of healthcare data governance presents its unique challenges. Using system dynamics in big data analytics can significantly improve healthcare management, decision-making, and patient outcomes. System dynamics is a modeling technique that helps understand complex systems, such as healthcare systems, by representing them as interconnected feedback loops and variables. When applied to big data in healthcare, it can provide valuable insights and support evidence-based decision-making. Addressing these risk factors is imperative to ensure big data's success and ethical use in healthcare through system dynamics.