Beyond HIPAA: Rethinking Health Data Governance in the Age of Machine Learning

With the spread of Machine Learning (ML) and Artificial Intelligence (AI) in the healthcare sector, the regulatory structures that currently dominate the field (particularly HIPAA law in the United States) have become unable to deal with emerging challenges and opportunities offered by health data utilization. This paper argues that the growing trend of predictive analytics and algorithmic evaluations needs governance models that go beyond the standard perimeter-centric and consent-based paradigm of HIPAA. By identifying key governance issues, such as algorithmic bias, data provenance, dynamic consent, federated learning architectures, and power asymmetries in the data ecosystems, the article suggests a multi-layered governance model, which should be based on the principles of accountability, fairness, transparency, data stewardship, and multi-stakeholder co-regulation. Along with the application of this model in clinical diagnostics, public health research, and business analytics, periodic reassessment of health data regulations is highly emphasized to protect health data privacy.