AI-Enhanced Data Governance: Automating Compliance In Healthcare Analytics Platforms

Abstract

Automating compliance with complex, evolving, multi-jurisdictional privacy laws, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, remains an open problem. Current solutions obtain some level of compliance but do not meet the complete requirements of these regulations. Advances in hardware and software support the argument that a new architectural approach is feasible. A broad architecture for AI-enhanced data governance has already been developed, clearing the way to addressing compliance automation in the context of Healthcare Analytics Platforms. Data governance is a primary focus; the deployment of such platforms tends to shift the cost–benefit structure of using personal data in data analytics. However, privacy laws remain fragmented and complex, and ensuring actual compliance still requires a high level of effort. AI-enhanced data governance has been proposed as a way to reduce the burden of performing the tedious, low-value tasks that support compliance. A simplified conceptual model of a Healthcare Analytics Platform highlights the areas of responsibility required to maintain privacy.

Healthcare service providers (HSPs) need to perform data analysis tasks that may require sharing the data they collect with external third parties. Using anonymization techniques may not be sufficient to protect the information of users of HSPs. Consequently, legislation such as HIPAA and the Personal Information Protection and Electronic Documents Act (PIPEDA) concentrates on how the data held by a HSP is used and by whom. AI-enhanced data governance can reduce the workload associated with ensuring compliance with such laws.