AI-Powered Data Transformation Accelerating Digital Quality

October 24, 2025 · Guest Contributor

The accelerating evolution of health care data has added new regulatory programs that support greater data access, interoperability and transparency. NCQA is shifting toward digital-only HEDIS^® reporting, and CMS is reinforcing data-driven measures. This shift will end manual abstraction and siloed data, and allow improved patient experience and health outcomes and a more efficient quality reporting process. Organizations must adapt to this new world.

AI and the HL7^® FHIR^® standard are two opportunities for the digital transition. Although AI accelerates clinical evidence detection, can summarize complex records in seconds, streamline reporting and automate repetitive tasks, health care demands near-zero tolerance for errors. AI thrives on probabilities, but patients and regulators require precision.

To ensure an accurate and precise AI strategy for digital quality, health plans need accurate data transformed into a common format—FHIR—as well as trusted and transparent models that are explainable with a human-in-the-loop design.

Today, clinical data processed by health plans are housed across a patchwork of mostly unstructured applications and vendor processes. Payers are racing to retrofit systems, and plans struggle to comply with the new rules and make “meaningful use” of mandated APIs.

Clinical Data Transformation: Interoperability 3.0

Interoperability 3.0 represents a transformational shift for health care, moving beyond standards and compliance mandates, and ushering in AI-powered platforms capable of processing all health data, regardless of structure or format. Integrating AI across clinical data drives significant value, from medical record review efficiencies to generating new member insights from previously unused data.

Interoperability 3.0 technology platforms can complete the industry’s transition to FHIR, enable real-time data quality monitoring, support interoperable data exchange from any source and drive performance for multiple data-driven programs—via a single data platform. Health plans should consider the following as they elevate their clinical data transformation strategy:

• Interoperability readiness. Collaborate with business and technology stakeholders to strengthen data infrastructure and ensure systems can extract value from all data sources and formats.
• Clinical source expansion. Collaborate with EHR vendors, ROI partners, national networks and APIs to store digital and traditional clinical data in a unified, accessible environment.
• Enable FHIR. Eliminate siloed information, extract and normalize disparate data sources and documents to a common standard such as FHIR.

The future of HEDIS is focused on digital quality. Plans that proactively invest in digital transformation and data quality, that unify and modernize their data infrastructure, will be best positioned for success.

The Role of FHIR in the Transition to Digital Quality and Trusted AI

Data is the fuel for a successful AI strategy. HL7 FHIR provides the foundation to make data interoperable and usable across systems, giving plans an accelerated solution to enable high-value delivery of accurate and timely insights. As plans transition to the FHIR standards, they need tools that:

• Validate and standardize data bundles.
• Transform non-FHIR input into compliant formats.
• Test conformance against implementation guides.

Plans without FHIR capabilities should use transformation tools to enable ingestion and conversion of data from any format to FHIR, as well as tools for the plan’s IT team to visualize and validate evidence to optimize its use for digital quality. Although challenges still remain, especially around versioning and vendor variation, FHIR readiness is no longer optional: It is required for any scalable digital quality data initiative.

Where AI Creates Value

Vital to the success of any AI strategy in the transition to digital quality is data quality. Health plans need an enterprise approach to clinical data transformation, where all data formats are considered and processed through a centralized entry point that can be scaled to ingest any data format, normalizes data to the FHIR standard and leverages multi-modal AI to deliver value across all applicable use cases.

When paired with FHIR infrastructure, AI delivers value through:

• Evidence extraction from both structured and unstructured data.
• Summarization of medical records to help clinicians review faster.
• Automation of low-risk workflows such as prior authorization.
• Deduplication of medical records across various use cases.

The Future of Quality

As MY 2025 ushers in expanded digital measures and equity-focused updates, organizations should prioritize readiness and collaboration with data partners to meet evolving standards. In short, digital transformation is no longer optional; it’s the key to sustainable, scalable quality performance in a rapidly shifting landscape.

This blog is brought to you by Tenasol and the views expressed are solely those of the sponsor.

HEDIS^® is a registered trademark of the National Committee for Quality Assurance (NCQA).

HL7^® and FHIR^® are registered trademarks of Health Level Seven International, and their use does not constitute endorsement by HL7.