The Risk of Poor Data in AI-Driven Care
In health care, artificial intelligence (AI) holds incredible promise—for speeding workflows, improving patient care, and strengthening financial performance. But as Todd Goughnour, MBA, RHIA of e4health explains, AI’s value depends entirely on the quality of the data that feeds it.
When patient information is incomplete, inconsistent, or inaccurate, AI systems produce unreliable results, erode trust with clinicians and patients, and can even compromise safety. This is where health information management (HIM) professionals play a crucial role.
The Trust Factor: Data Quality Is the Foundation
Goughnour emphasizes that trust—among providers, clinicians, staff, and patients—is essential for realizing AI’s benefits in health systems. Accurate data isn’t just a technical requirement; it’s what enables AI to deliver meaningful insights rather than misguiding users and creating risk.
HIM professionals, with their expertise in data governance and standards, are ideally positioned to lead data quality efforts as organizations adopt advanced technologies.
Three Strategies HIM Leaders Can Use
1. Understand the Stakes & Leverage Best Practices
HIM professionals must educate themselves on how data quality affects AI, including risks related to bias, privacy, and data gaps. Goughnour recommends leveraging authoritative guidance from organizations such as The Joint Commission, the AMA, and the National Academy of Medicine, along with established AI ethics frameworks.
2. Find Data Quality Gaps and Communicate Risk
Common sources of poor data include system mergers and EHR conversions, patient self-registration errors, and manual entry inconsistencies.
- Duplicate or mismatched patient records
- Inconsistent documentation workflows
- Unclear data governance ownership
HIM leaders should translate these issues into executive-level concerns by clearly tying data quality problems to patient safety, revenue loss, denial risk, and legal exposure.
3. Build a Culture of Continuous Improvement
High-quality data requires ongoing monitoring. AI systems evolve, and fragmented records across multiple platforms increase the risk of duplication and error.
- Proactive cleanup of data before clinical use
- Cross-department collaboration between HIM, clinical, and revenue cycle teams
- Education to reinforce shared responsibility for data integrity
Bottom Line
AI can transform health care—but only if it’s built on trusted data. HIM professionals are uniquely qualified to ensure information remains accurate, complete, and reliable.
By educating themselves, identifying data quality gaps, communicating strategic risk, and fostering continuous improvement, HIM leaders can make data integrity a strategic priority and unlock AI’s full potential in patient care and organizational performance.
To read the whole For The Record article, click here .