Data Analytics

Healthcare Data Governance Is the Foundation of Modern Medical Research

A strong data management system is essential for accelerating artificial intelligence-powered medical breakthroughs while maintaining HIPAA compliance and patient trust.

Erin Laviola

Erin Laviola is a freelance writer who produces content for academic medical centers and healthcare-focused organizations.

With support from artificial intelligence, the possibilities for research advancements are endless. AI tools can review and process information, find patterns and make predictions faster than humans can, with the potential to expand our understanding of complex conditions and accelerate the development of lifesaving therapies.

However, just like any other software, AI is only as reliable as the data sets it pulls from. A solid data governance framework — referring to the standards and policies that dictate how data is collected, organized, stored and shared — provides the infrastructure necessary to make medical breakthroughs possible.

“Data is fundamental to advanced analytical technology,” says David Ebert, chief AI and data science officer at the University of Arizona. “Good data governance allows you to implement AI and use it for research in a responsible way that gets trustable results.”

EXPLORE: How does minimum viable data governance enable smarter healthcare?

Core Components of a Healthcare Data Governance Framework

To make meaningful progress in healthcare research, a strong data governance framework will include:

Clear data ownership, or defining which team is responsible for oversight of a particular data set’s accuracy and security
Strong privacy controls, including encryption
Shareability policies that balance innovation with compliance
Quality management processes including continuous monitoring to catch biases and mistakes early
Standardized definitions so that different teams can communicate effectively
A data catalog to help researchers easily locate and utilize data
Metadata management, which helps AI tools interpret data accurately
Data lifecycle management policies

Amy Trainor, system vice president and CIO at Ochsner Health and a registered nurse, emphasizes that good data management must be embedded in the organization’s culture. Every team member, whether involved in patient care, medical education or administration, has a role in both safeguarding data and ensuring its accuracy.

“Governance cannot sit off to the side as a policy binder. It has to show up in how data is defined, accessed, protected, measured and used every day,” Trainor says. “When data is incomplete, inconsistent or poorly defined, it introduces risk to the integrity of the research.”

Click the banner below to find the right strategy to turn data into insights.

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.”

Governance cannot sit off to the side as a policy binder. It has to show up in how data is defined, accessed, protected, measured and used every day.”

Amy Trainor System Vice President and CIO, Ochsner Health

Assessing Your Organization's Data Governance Maturity

“Organizations should start by honestly assessing whether they trust the data used to make important decisions,” Trainor says. “Start by identifying a high-priority business, research or AI initiative and evaluate whether your existing data governance capabilities support that effort.”

To build an effective data governance infrastructure, health systems also need to include all necessary stakeholders. A multidisciplinary review of how the data will be collected, organized and used should include representation from clinical, legal, information services and auditing teams.

Getting buy-in from faculty, staff and partners is another critical piece that Ebert says many organizations miss.

“What I’ve found is the most errors in data systems tend to be from one-way reporting systems, where people input data but never see how it’s used and therefore don’t see the value. The sharing and feedback loop is really important for good governance,” Ebert explains.

“The organizations that will lead the next era of healthcare innovation will be the ones that can balance two responsibilities at the same time: protecting patient data while enabling research, analytics and AI that improve lives,” Trainor says. “Those are not competing priorities. Done well, they reinforce one another.”

gorodenkoff/Getty Images