U.S. data flaws threaten healthcare AI goals — commentary

NEVADA, UNITED STATES — The United States is racing to deploy artificial intelligence (AI) across healthcare, but experts warn that the country’s fragmented health data systems could undermine those ambitions unless hospitals and health systems address the foundational infrastructure needed to support AI-driven care.

A recent commentary published in Healthcare IT Today highlights the growing gap between AI innovation and the underlying “plumbing” of the U.S. healthcare data ecosystem. 

The piece was co-authored by Richard Ricciardi, Professor and Executive Director of the Center for Health Policy and Media Engagement at The George Washington University, and Michael Savas, a fellow at the Center.

The authors state that healthcare data in the U.S. continues to exist in separate systems as both technology companies and policymakers actively promote AI for faster medical diagnoses and better treatment methods.

“For nearly two decades, vast amounts of patient data have been locked inside disconnected health systems, electronic health records, and research silos,” the authors wrote. 

They added that this fragmentation has created systems “optimized for billing and market share, not learning.”

That structural weakness could limit the ability of hospitals and clinics to harness AI tools that rely on large volumes of integrated, high-quality patient data.

How global outsourcing solves healthcare AI data gaps

The growing recognition of this infrastructure gap is also reshaping the role of global healthcare outsourcing.

While U.S. health systems have historically used offshore partners for cost-effective medical billing and administrative support services, today’s outsourcing providers now assist organizations in building essential operational and technical structures required for data-driven healthcare delivery.

For hospitals that explore AI initiatives, offshore partners provide critical assistance with data normalization, electronic health record (EHR) integration, and analytics preparation. This groundwork enables AI systems to successfully extract insights from clinical data scattered across different platforms.

The need for stronger data systems is becoming more urgent as federal policymakers push AI innovation. One recent executive directive, titled “Unlocking Cures for Pediatric Cancer with Artificial Intelligence,” calls for deploying AI to improve the diagnosis and treatment of childhood cancers.

However, without modernized data infrastructure, the authors warn that the potential impact of such initiatives will remain limited. 

“Artificial intelligence depends on large volumes of high-quality, diverse data to detect patterns and improve predictions, a particularly powerful proposition in healthcare,” the authors noted.

Building AI-ready healthcare systems with offshore partners

To address these challenges, many healthcare organizations are turning to global partners for specialized support that extends well beyond traditional outsourcing.

Offshore teams increasingly provide assistance with data curation for AI models, interoperability support, human-in-the-loop clinical review, and omnichannel patient engagement. These capabilities become essential for health systems that need to expand their AI operations in a secure and efficient manner.

Furthermore, these services help providers prepare data for technologies such as federated learning, an approach highlighted in the commentary. The method allows AI models to train on patient data stored within hospitals while sharing insights across institutions without exposing sensitive records.

“Federated learning is not just a technical solution; it is a bridge from fragmentation to collective insight,” the authors wrote.

For healthcare providers, building that bridge may require partnerships that combine clinical expertise, technology integration, and scalable operational support.

The authors warned that without addressing the data foundation behind AI, the U.S. risks missing a critical opportunity to transform care.

“Without modernizing how the health care system learns from its own data, the United States risks squandering one of the most consequential opportunities in modern healthcare,” the authors concluded.