Applications of AI in Electronic Health Records, Challenges, and Mitigation Strategies

The integration of Artificial Intelligence (AI) into Electronic Health Records (EHRs) offers the potential to revolutionize healthcare by improving patient outcomes, enhancing clinical decision-making, and automating administrative tasks. However, this integration presents several challenges, including data privacy concerns, bias in AI algorithms, interoperability issues, and regulatory compliance hurdles. This paper reviews the current applications of AI in EHRs, particularly in predictive analytics, Natural Language Processing (NLP), and Clinical Decision Support Systems (CDSS). It also provides a detailed analysis of the challenges and mitigation strategies associated with these applications. Mitigation strategies such as advanced encryption methods, FHIR standards for interoperability, algorithmic auditing, and automated compliance tools are explored in-depth. Through a comparative review of existing literature, this study highlights both the opportunities and challenges of integrating AI with EHRs, proposing solutions to overcome the technical, ethical, and regulatory barriers. By addressing these issues, AI can lead to more efficient, secure, and equitable healthcare delivery.