Data Analytics
HIMSS25: How To Design Effective Data Governance in Healthcare
Artificial intelligence can only be effective if the data it’s trained on is accurate and high-quality. For healthcare organizations considering AI applications involving the use of data, it might be time to revisit their data governance.
At HIMSS25 in Las Vegas, HealthTech spoke with healthcare data leaders on the key factors needed for data governance success, as well as best practices, the relationship between AI and data governance, and how to prevent bias in data.
DISCOVER: Taking advantage of data and artificial intelligence can improve healthcare outcomes.
Check out this page for our complete coverage of HIMSS25. Follow us on the social platform X at @HealthTechMag and join the conversation at #HIMSS25.
Participants
Glenn Wasson, Administrator of Analytics, UVA Health
Lee Pierce, Healthcare Strategist, CDW
Video Highlights
- Data governance should be inclusive of leaders from across the organization in addition to being comprehensive.
- Getting buy-in from senior leadership on the value of data and data quality is critical for the success of AI projects.
- There are different kinds of bias that require different types of mitigation tactics. The important thing is to continue to ask about bias.
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