Healthcare Data Governance: Enabling AI-Powered Medical Research

How Data Governance Enables AI and Research at Scale

Data governance can help expedite groundbreaking research because AI tools can provide real-time analysis of complex, population-level data sets. This improved statistical power allows researchers to find patterns and anomalies that might be missed in smaller sample sizes, allowing innovative treatments to reach patients faster. 

Recent progress in precision medicine, especially in gene-targeted therapy, is an example of how data governance can support medical advancements. With the help of AI models, clinical research teams are identifying disease-causing mutations across diverse populations and developing more precise gene editing techniques that can be tailored for individual patients.

“The more data you can utilize, the quicker advances can take place,” Ebert says. “It allows for more sample points, so you’re able to do better prediction and correlation and really fine-tune the results.”

He and Trainor both stress, however, that sheer volume of data alone isn’t enough to scale research. 

“For AI in particular, governance creates the trust layer,” Trainor says. “It helps teams know whether the data is appropriate for the use case, whether it reflects the population we serve, whether the outputs can be validated and whether the right human accountability remains in place.”

DISCOVER: Manage artificial intelligence risks through AI and data governance.

HIPAA Compliance and Data Governance

Protecting sensitive patient information is a key function of data governance. With more than 700 large healthcare data breaches happening every year, health networks are under pressure to improve data security. To ensure HIPAA compliance, organizations should have in place:

• De-identification processes
• Access controls and audit logs
• Clear policies for data retention, storage and disposal
• Risk assessments to identify vulnerabilities
• Regular workforce training on appropriate data handling
• Oversight of third-party vendors to ensure they meet the same privacy and security expectations

However, healthcare data management isn’t only about security. Ebert stresses that for AI tools to effectively support medical research, data systems must be accessible.

“Traditionally, a lot of people think of data governance as making it as restrictive as possible,” Ebert says. “There needs to be higher-level management involved in finding the right middle ground between use cases and protection.” 

To remain HIPAA compliant without hindering progress, Ebert recommends expanding the scope of how a patient’s de-identified data will be used. Rather than asking for permission to use their information for a single study, organizations should ask for consent to use the data for “all research at this hospital.”

Ebert adds that destroying patient data after an individual study is completed “hamstrings the ability to innovate effectively. It makes protection the simplest, but then researchers can’t benefit from all of that detail in follow-up work.”