Health August 12, 2025

The role of data quality in building a robust health tech ecosystem

By: Cheryl Mason
Portrait of mature female doctor using digital tablet with doctor's conference / meeting on background
Explore how high-quality data and semantic interoperability power the CMS Interoperability Framework and innovation in the health tech ecosystem.

Healthcare technology is set to undergo a monumental transformation, moving from a focus on EHR functionality to a focus on API exchange. At the heart of this evolution lies interoperability, the ability to seamlessly share and use data across different systems. The CMS Interoperability Framework could be emerging as a pivotal force driving this change. But there are other forces at play. By emphasizing shared standards and collaboration among patients, providers, payers, and digital health technologies, these forces are poised to evolve the health tech ecosystem.

Yet, even as some healthcare organizations adopt robust data exchange pathways, the ultimate goal, the last mile as it were, semantic interoperability, remains out of reach for many. While data exchange has improved, the next challenge is making that data complete, meaningful, and actionable

Below, we’ll explore core aspects of the CMS Interoperability Framework and other federal actions, the connection to semantic interoperability, and why high-quality data underpins the digital transformation of healthcare.

Understanding the CMS Interoperability Framework

The CMS Interoperability Framework serves as a voluntary roadmap for organizations committed to advancing healthcare data exchange. This framework prioritizes a patient-centered approach and encourages participation from all healthcare stakeholders. By focusing on standardization and enabling technologies, CMS aims to foster seamless collaboration across the health tech ecosystem. Early adopters span the healthcare ecosystem with promises to share data and build patient-facing apps in three topical areas.

The goals of this initiative are in line with the RFI on the Health Data Ecosystem that the CMS and ASTP released earlier this year. This RFI signaled the Federal Government's intent to cultivate a collaborative digital health ecosystem that builds on prior efforts like Blue Button 2.0 and Data at the Point of Care.

Key components of the CMS Interoperability Framework

  1. Patient empowerment: A defining features of this framework is empowering patients with access to their complete medical data. Built on digital identify and consent management principles, the framework ensures individuals have direct control over their information. Patients can make informed decisions, share data seamlessly with their providers, and engage more actively in their care.
  2. Provider access at the point of care: The framework supports providers with access to critical patient information through interoperable digital tools. Such access reduces administrative burden, aids clinical decision-making, and ensures continuity of care. Providers must ensure that they are accessing the information for treatment purposes only. 
  3. Data availability protocols: Adopting standards like FHIR APIs (Fast Healthcare Interoperability Resources) and USCDI (United States Core Data for Interoperability) ensures that data is exchanged in compatible formats. By 2026, Organizations that attest to alignment with the CMS Interoperability Framework are expected to comply with its standards and undergo review.

Additional federal actions bolstering FHIR adoption

The CMS Interoperability Framework complements several federal initiatives:

  • The ONC’s 2020 Cures Act Final Rule and the complementary CMS Interoperability and Patient Access Final Rule both introduce the need to capture and share data using the United States Core Data for Interoperability and FHIR-based APIs.
  • TEFCA, introduced in the 2020 Cures Act and Final Rule, and implemented in December 2023, ushered in a new era of nationwide exchange of data.
  • The 2024 CMS Interoperability and Prior Authorization Final Rule promotes the use of FHIR to streamline prior authorization processes while improving patient and provider experiences.
  • The ASTP/ONC HTI-4 Rule emphasizes prescription drug cost transparency and focuses on prior authorization using the Da Vinci implementation guides.

Each of these and the new CMS interoperability Framework underscores a shared goal, each focusing on important use cases, but they also reveal a significant limitation. Without semantic normalization, the data exchanged, even using FHIR, often lacks standardization and usability, limiting its potential to improve outcomes.

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The role of semantic interoperability in the health tech ecosystem

While foundational interoperability ensures data transmission, semantic interoperability creates actionable intelligence. It’s the point at which data not only moves between systems but is understood in the same way by all parties, enabling actionable insights, clinical decision-making, and patient-centered care.

Semantic interoperability ensures that data exchanged between systems retains its meaning, regardless of the source or destination. It goes beyond just transmitting data (foundational interoperability) or formatting it consistently (structural interoperability). For instance, a blood glucose reading labeled “BG” in one system and “Blood Glucose” in another may be structurally interoperable but not semantically aligned, leading to potential misinterpretation.

Semantic interoperability involves:

  • Standardized terminologies: Using vocabularies like LOINC, SNOMED CT, RxNorm, and ICD-10.
  • Codification: Assigning codes accurately to clinical concepts so they can be interpreted uniformly.
  • Contextualization: Embedding metadata (e.g., time, source, clinical relevance) to preserve meaning.

Why semantic interoperability matters


AI-Driven insights

AI tools need clean, structured, and clinically validated data to deliver accurate and personalized insights. When data is semantically interoperable, AI can identify patient risks, tailor treatment plans, and support clinical decisions with confidence. This includes referencing up to date value sets, such as LOINC for lab results or SNOMED CT for clinical findings, ensuring that AI models interpret data consistently across systems. Without them, even advanced technologies like conversational AI may fall short of their goal to empower patients and improve engagement.

Operational efficiency

Standardized data helps providers avoid unnecessary repeat tests, missed results, and administrative delays. Semantic interoperability ensures that every piece of information, whether it’s a lab result or a medication list, is complete, accurate, and ready to use. This is essential for initiatives like “Kill the Clipboard,” which aim to eliminate outdated, manual processes in favor of seamless digital workflows.

Error prevention in care delivery

Inconsistent or poorly coded data can lead to dangerous gaps in care. For example, managing chronic conditions like diabetes depends on tracking lab values like A1C over time. If those results aren’t standardized, they can be misinterpreted or missed entirely. Semantic interoperability ensures that critical data is consistently codified and understood, reducing medical errors and enabling proactive, personalized care. The goal of launching personal health apps focused on Diabetes and Obesity can only deliver results if the data being used is codified consistently using agreed-upon standards and rules for normalization.

Achieving true semantic interoperability depends on robust data quality, a factor critical for meeting the goals of the CMS Interoperability Framework and other federal initiatives.

Protecting sensitive data in a connected ecosystem

In a fully interoperable health tech ecosystem, the stakes for data protection are higher than ever. Sensitive data, ranging from genomic information to behavioral health records, must be handled with precision and care. Semantic interoperability helps ensure that sensitive data is properly labeled and understood across systems. Data quality tools must support sensitive data masking, context-aware tagging, and compliance automation. Patient trust depends on transparent data practices, especially as consumer-facing apps and APIs become more prevalent.

The CMS Interoperability Framework and beyond: Preparing for the future of healthcare

The CMS’s 2026 FHIR compliance milestone calls for proactive preparation. Organizations must develop strategies to facilitate seamless data sharing while prioritizing governance and security. The Health Language platform delivers value through:

Terminology management at scale: Centralized terminology management that ensures consistent use of clinical vocabularies like SNOMED CT, LOINC, RxNorm, and ICD-10 across systems. This enables semantic normalization at the point of data ingestion and exchange.

Automated mapping and normalization: Through automated mapping tools, the Health Language platform translates local codes and non-standard terms into standardized terminologies, reducing manual effort and improving data usability for analytics, AI, and patient-facing applications.

Support for FHIR and USCDI standards: The Health Language platform is designed to support FHIR APIs and align with USCDI data elements, ensuring that structured data exchanged via CMS-aligned networks is both compliant and semantically interoperable. 

Final thoughts on building a thriving health tech ecosystem

The CMS Interoperability Framework offers a pathway to advancing collaborative healthcare data exchange, focusing on patient and provider access. However, achieving semantic interoperability is essential for unlocking its full potential. High-quality, standardized data not only empowers innovation but also transforms the health tech ecosystem by fostering better care, improved outcomes, and sustainable success.

For healthcare organizations, the time to act is now. By prioritizing data quality and aligning efforts with the CMS framework, they can overcome interoperability challenges and lead the next generation of healthcare transformation.

Learn how the Health Language platform can strengthen your data quality efforts and streamline your interoperability strategy for the future. Speak to an expert today to learn more.

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Cheryl Mason
Cheryl Mason
Director, Content and Informatics, Health Language
As the Director of Content and Informatics, Cheryl supports the company’s Health Language solutions leading a team of subject matter experts at that specialize in data quality. Together, they consult with clients across the health care spectrum regarding standardized terminologies, data governance, data normalization, and risk mitigation strategies.
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