11 Nov Trustworthy Healthcare AI Starts with Reliable Data

Top Takeaways

• Americans are open to AI in healthcare but want human oversight, particularly when technology influences medical decisions.
• Reliable, accurate patient data is essential for building confidence in AI-powered healthcare applications.
• Human expertise in data quality assessment and validation helps ensure healthcare information accurately reflects patient records.
• IMAT Intelligence combines curated healthcare data, human oversight and validated processes to support trustworthy analytics and AI readiness.

Artificial intelligence is creating new opportunities to improve healthcare, from reducing administrative burdens to supporting clinical decisions. But as adoption accelerates, an important question remains. How can healthcare organizations ensure that AI is working with information they can trust?

A new Pew Research Center survey offers insight into how Americans view this challenge. The October 2026 report, based on responses from 3,488 U.S. adults, found that people are generally open to AI performing certain healthcare tasks, particularly when humans remain involved in reviewing its work.

For example, 60% of respondents were open to AI analyzing medical scans when a human conducts the final review, compared with just 6% who supported AI performing the task independently. The survey also found that 60% were uncomfortable with their doctors using AI for diagnoses and treatments.

These findings highlight the importance of human oversight in healthcare AI. But oversight should begin well before an AI system produces a recommendation or insight. It should also extend to the quality and reliability of the data supporting those decisions.

Why Data Quality Matters for Healthcare AI

AI applications depend on the information they receive. When patient records are incomplete, inconsistent or inaccurate, the resulting analysis can be misleading, regardless of how sophisticated the technology may be.

Healthcare organizations routinely work with information from electronic health records (EHRs), claims systems, laboratories and other sources. These datasets often contain different formats, coding structures and levels of completeness.

Without proper aggregation, standardization and validation, organizations risk introducing errors into the information used for analytics and AI applications.

For example, an AI tool designed to identify patients who may benefit from additional care management could overlook important clinical details if records are fragmented or incorrectly matched.

Building trustworthy healthcare AI therefore requires more than connecting disparate systems. Organizations must ensure that patient information has been properly curated and accurately represents the underlying clinical reality.

Human Oversight Starts with Data Quality

The Pew findings emphasize the public’s preference for human involvement in healthcare decisions. That same principle is important when preparing the data used by AI systems.

Automated tools can help identify inconsistencies, missing information and potential errors across large healthcare datasets. However, technology alone cannot always determine whether information accurately reflects a patient’s clinical circumstances.

Human expertise provides an additional layer of review, particularly when data quality issues require clinical interpretation or technical investigation.

This approach is central to IMAT Solutions’ Healthcare Data Quality Assessment, which incorporates oversight from highly qualified software engineers and clinical data specialists.

By combining technical expertise with clinical knowledge, IMAT helps organizations evaluate data quality, identify discrepancies and improve confidence in the information supporting healthcare analytics and AI applications.

The goal is not simply to process more data. It is to ensure that the information being used is meaningful, reliable and grounded in actual patient records.

Building Confidence Through Validated Data

As healthcare organizations expand their use of AI, consistent data quality practices become increasingly important.

Reliable information requires processes for identifying errors, validating records and maintaining data consistency across multiple sources. These capabilities help organizations establish a dependable foundation for clinical analytics, quality measurement, population health management and emerging AI applications.

IMAT Intelligence supports this foundation by aggregating, normalizing and validating healthcare data to produce curated information that reflects patients’ actual health records.

IMAT Solutions has also earned the Validated Data Stream designation through the National Committee for Quality Assurance (NCQA) Data Aggregator Validation (DAV) program. This designation recognizes the quality and integrity of clinical data streams, as well as the processes and systems used to manage and safeguard that information. By combining DAV-validated data with human oversight and rigorous quality assurance, IMAT helps healthcare organizations build greater confidence in the information supporting analytics, quality measurement and emerging AI applications.

Combined with human oversight, these capabilities help organizations address data quality challenges before unreliable information affects downstream analytics or AI-generated insights.

While no data management process can eliminate every potential risk associated with AI, establishing a consistent approach to data validation and quality assessment is an important step toward more responsible adoption.

Trustworthy AI Begins with a Strong Data Foundation

The Pew survey reinforces a message that healthcare organizations should keep in mind as they expand AI adoption. People may be willing to embrace new technology, but they still want confidence that important healthcare decisions involve appropriate human judgment.

That confidence depends in part on the reliability of the information supporting those decisions. By prioritizing curated patient data, human oversight and consistent validation practices, healthcare organizations can build a stronger foundation for AI while improving the quality of their existing analytics and decision-making processes. 

Learn More About IMAT Intelligence

Discover how IMAT Intelligence and IMAT’s Healthcare Data Quality Assessment can help your organization improve data reliability, strengthen quality assurance and prepare for AI-driven healthcare applications. Contact IMAT Solutions to learn more.

 

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