Prioritize Caring Algorithms and The Human Touch

By Rachel Podczervinski MS, RHIA, Senior Vice President, and Julie A. Pursley, MSHI, RHIA, CHDA, FAHIMA, Director of Industry Relations, Harris Data Integrity Solutions

Accurate data is the backbone of health information systems—and increasingly, organizations are turning to AI to clean, manage, and match that data. While automation can improve data quality, it doesn’t fully solve the deeper challenge of data integrity. In fact, a Black Book Research survey found that 92% of early AI adopters felt their systems were “not accurate or actionable enough for clinical use.”

Patient identification is especially complex. Each healthcare journey is unique, and our digital identities should reflect that. Yet no current solution achieves a perfect match rate. Many organizations rely on automated tools to link records without human review. Still, these systems only reach a certain level of accuracy, leaving gaps that can compromise patient safety, care coordination, and compliance.

AI holds promise for improving person-matching, but it must be paired with thoughtful governance. Caring Algorithms®—designed to prioritize safety and contextual nuance—offer enhanced matching capabilities. When combined with human-in-the-loop oversight, they form a foundational strategy for trustworthy identity management.

Human judgment remains essential. As algorithms flag potential matches, trained professionals guide the development, deployment, and refinement of AI tools across the enterprise. With the proper guardrails in place, organizations can reduce costs and streamline administrative tasks—without sacrificing accuracy or trust.

Entrusted data is the key to unlocking AI’s full potential. A future of secure, reliable patient identification is within reach—with the right balance of technology and human insight.

Read “Prioritizing Caring Algorithms and the Human Touch,” the final installment of “Entrusting Data in an AI World: A Framework for Accurate Healthcare Data,” a 3-part white paper on the impact of AI on person matching.