ENTERPRISE AI ANALYSIS

The integration of technology into healthcare workflows has always necessitated robust governance and risk management practices. Historically, the adoption of new technologies, such as electronic health records, medical devices, and telemedicine, was accompanied by the development of governance frameworks that were designed to ensure patient safety, data security, and regulatory compliance. These frameworks have evolved to address the unique challenges posed by technological advancements necessary often to respond to regulatory demands such as safeguarding patient privacy under the Health Insurance Portability and Accountability Act; ensuring the safety and efficacy of medical devices regulated by FDA; and, most recently, supporting the adoption of trustworthy AI in healthcare settings mandated by the Office of the National Coordinator Health Data, Technology, and Interoperability (ONC HTI-1) rule.

Risks in healthcare AI applications can emerge at various stages, from data collection to algorithm implementation. One key area of concern is data quality. AI models rely heavily on the data they are trained on, and if this data is incomplete, biased, or unrepresentative, the resulting predictions or recommendations can be flawed. Algorithmic bias is another significant risk. Interpretability is another challenge in the governance of AI applications in healthcare. Finally, implementation risks emerge when AI systems are integrated into healthcare workflows without sufficient planning and oversight.