healthcare organizations can adopt AI without compromising trust or data protection by building defensible AI platforms. This involves three core concepts: scalable AI management, secure insights, and effective platform design.
Scalable AI management focuses on control management, which includes mapping controls and requirements against best practice standards and functional needs, and continuously monitoring those controls. This ensures AI systems perform optimally within a framework aligned with regulatory and patient expectations. Key steps include identifying necessary controls, translating them into technical specifications, and establishing operating guidelines for ongoing management.
Secure insights address the challenge of balancing AI utility with data security. The approach uses synthetic trends within abstracted data spaces to enhance machine learning models without compromising data security. This method preserves data utility for AI while protecting sensitive information, significantly reducing reconstruction risk.
Effective platform design involves creating a balance between central control and decentralized innovation. A "secure health fabric" architecture, combining data mesh and data fabric principles, allows independent teams to develop and manage their own AI and data products within segregated workspaces, each governed by rigorous protocols and human-in-the-loop checkpoints. AI agents operate within predefined guardrails to automate tasks while preserving oversight.
A case study demonstrates how synthetic health trends increased machine learning precision by over 16x in a federated architecture, unlocking revenue growth potential 10x greater than previously possible. The platform uses a hybrid model of federated learning, combining synthetic trends across different functional areas under strict security controls.
Key conclusions emphasize the importance of embedding defensibility into data systems. By adopting scalable AI management, robust data security practices, and thoughtfully designed data architectures, healthcare organizations can drive innovation while upholding patient care and trust. The IQVIA Synthetic Trends Engine and secure health fabric provide tools for implementing these principles, ensuring responsible AI use aligned with privacy and security standards.
