The role of AI and data integration in enhancing data protection in U.S. digital public health an empirical study

Fast digitization of public health services make strong data protection procedures necessary now. Not only has the growing use of digital tools, like electronic health records (EHRs) and telemedicine platforms created an urgent need for high tech tools to protect sensitive health information but even conventional health record systems with sensitive health information tend to utilize these tools. In this investigation we examine the effect of artificial intelligence (AI) and data integration on building data protections frameworks in the US digital public health system. In an empirical analysis we identify the ways in which AI driven technologies will help in the process of threat detection, compliance monitoring and incident response automation. Further, the study of the potential for data systems integration to address the security challenges stemming from data silos and fractured infrastructure is addressed. Case studies, survey data from healthcare IT pros, and public breach reports were analyzed to reveal significant benefits, challenges, and best practices. Our results provide practical guidance to decision makers, healthcare entities and technology developers in building a safe, robust and fair digital health environment. Furthermore, the document calls for closing resource gaps for smaller entities and fighting biases in AI systems. It establishes the groundwork for future work to protect digital public health information.