Re-Thinking Our Approach to Population Health

NCQA’s Wellness and Health Promotion Accreditation and Certification program has been in place for more than a decade, helping organizations design programs that engage people in improving their health. In 2025, NCQA started the Innovations in Wellness and Condition Management Working Group to update the program to reflect best practices and new technologies for evaluating population risk and providing self-management and coaching to help improve health outcomes. This is part of a larger effort to evaluate how high-quality, effective population health services are delivered across NCQA programs.

“As an industry, we have an opportunity to redefine our approach to population health and how we prevent and manage chronic disease,” says Rachel Harrington, PhD, NCQA’s Senior Product Strategist. “We know that 40-60% of the factors that influence a person’s health come from outside the walls of the healthcare system. With limited resources, especially in primary care and behavioral health, it is important to help support people in managing their health, including the use of digital technologies.”

In 2026, NCQA will start shaping these new standards, focusing on evaluating outcomes and supporting confident decision-making on digital health and wellness solutions. This isn’t just a focus for NCQA. “It’s validating to see that our work aligns with the CMS Innovation Center’s new ACCESS model,” says Harrington. “We hope it will motivate organizations to innovate and improve the patient experience.”

Understanding Health Differences Within Populations and Communities

Improving population and community health requires organizations to identify variations in health outcomes, look for the root causes and target solutions to populations and communities. The upstream, structural and personal factors that drive differences in health outcomes are complex and multifaceted—and often require data, investments and partnerships broader than the healthcare system. NCQA’s Accreditations in Health Outcomes and Community-Focused Care give organizations a framework to understand differences and close gaps.

“We’ve updated our program to give organizations more ways to view population and community health and a greater ability to tailor the program to the areas most relevant to the populations they serve,” says Elizabeth Ryder, NCQA’s Assistant Director, Product Management. “For example, disability status is a new population focus for Health Outcomes Accreditation, which complements a new HEDIS^® measure that we introduced in measurement year 2026.”

Listen to our podcast, One in Four: Making Disability a Quality Priority, to learn more about these changes.

Shaping the Future of Primary Care

Primary care is evolving at an astounding pace. NCQA’s Patient-Centered Medical Home Recognition program laid the foundation by providing an operational and quality improvement framework for primary care. Now, we are helping practices advance their relationships with payers and succeed in value-based care.

“We are looking at the next horizon for primary care,” says Jeff Sitko, NCQA’s Assistant Vice President, Product Management. “We have an opportunity to create a best practice, scalable delivery model that provides a blueprint for primary care practices to continue developing their capabilities. We want to work side-by-side with practices to understand what’s valuable and realistic, while also reducing administrative burden.”

Stay tuned for an announcement about our primary care partners. In the meantime, listen to our Quality Matters podcast, What’s New and What’s Next for Primary Care.

Integrating Primary Care and Behavioral Healthcare

People with mental health conditions and substance use disorders are more likely to experience chronic health conditions like heart disease and diabetes. Similarly, people who are living with chronic conditions may struggle with depression or anxiety. Integrated care models that combine behavioral health and primary care can improve access and coordination, leading to better health outcomes.

“We need more care delivery models that support whole-person care,” says Julie Seibert, PhD, NCQA’s Assistant Vice President, Behavioral Health. “Integrating behavioral health and primary care can improve access and coordination of care by meeting people where they are and implementing a ‘no wrong door’ policy when it comes to accessing behavioral health services.”

In 2026, NCQA will continue to promote integration, with funding from the Health Resources and Services Administration, to support Federally Qualified Health Centers and Look-Alike Health Centers seeking NCQA’s Distinction in Behavioral Health Integration.

We’ve also updated our Behavioral Health Accreditation program to strengthen the focus on population health and network adequacy. Read our blog post, Behavioral Health Accreditation Promotes Accountability, to learn more.

Advancing the Transition to Digital Quality Measurement

The transition to digital quality measurement is accelerating as healthcare moves rapidly toward interoperability and real-time data exchange. Most HEDIS measures are available in a digital format, ready for implementation. From building CQL engines to integrating digital measures at the point of care, organizations are making progress and showing tangible results.

“The year 2030 is our north star to become fully digital, and industry alignment is critical for success,” says Tricia Elliott, NCQA’s Vice President, Quality Implementation. “There are three parallel tracks that need to converge for us to continue forward progress: updates to the CMS Digital Quality Measures Roadmap, conversion of data to the HL7^® FHIR^® standard and clarity on the use of USCDI Core versus USCDI QI Core standards. The more we can build alignment, the easier it will be for everyone to do the work we need to do by 2030.”

In 2026, we anticipate broader adoption of digital HEDIS measures, supported by certification and parallel testing. NCQA recently launched a Digital Quality Measure Evaluation Package that includes a sample of digital HEDIS measures and supporting tools to help explore, test and plan your transition with confidence. Our Digital HEDIS Directory highlights how organizations are using NCQA’s digital HEDIS measures to modernize care delivery, drive efficiencies and improve outcomes.

Visit NCQA’s Digital Quality Hub for more resources to support your transition.

Expanding Use of Clinical Data in HEDIS^®

HEDIS is evolving to provide a more complete picture of care for populations, enabled by increased integration of clinical data. In Measure Year  2026, we will implement six new Electronic Clinical Data Systems (ECDS) measures and three measures will transition to ECDS-only.

“While every organization is on its own journey in incorporating clinical data, what remains constant is the trust in a reported HEDIS rate,” says Taylor Musser, NCQA’s Director, Measure and Data Operations. “This is driven in part by the audit requirements holding all organizations to the same expectations for data contributing to HEDIS. While a measure may be new or updated, the HEDIS Compliance Audit^™ helps to ensure an apples-to-apples comparison as HEDIS evolves.”

Read our blog post, HEDIS MY 2026: What’s New, What’s Changed, What’s Retired, to learn more about what you can expect in 2026.

Improving Quality of Care for Patients with Cardiovascular-Kidney-Metabolic Syndrome

NCQA conducted three expert convenings in 2025 to gather insights and help define our quality measurement approach related to Chronic Kidney Disease and Cardiovascular-Kidney-Metabolic (CKM) Syndrome. We are excited to accelerate this work in 2026.

“We’re interested in exploring quality measures that focus on risk assessment and prevention because if you can prevent one of these CKM-related diseases, you can often prevent them all,” says Caroline Blaum, MD, NCQA’s Assistant Vice President, Chronic Conditions and Complex Care. “I anticipate that 2026 will be a year of significant progress as we define our measurement approach and begin testing with real-world data.”

NCQA recently released a white paper that synthesizes what we learned from the convenings and makes recommendations for a holistic approach to the prevention and management of CKM syndrome.

Defining High Quality Diabetes Care

Diabetes is one of the diseases intertwined within the CKM framework. NCQA’s Diabetes Recognition Program recognizes clinicians who use evidence-based measures to provide high-quality care to patients with diabetes. We added three new measures to the program for 2026: Statin Therapy Prescription, Depression Screening and Follow-Up and Continuous Glucose Monitoring (CGM) Utilization. The CGM Utilization measure is the first step toward understanding and quantifying the growing use of this technology.

“The new CGM Utilization measure will help us understand where the technology is being used, which populations are using it and whether there are barriers limiting adoption,” says Emily Hubbard, MPH, NCQA’s Senior Research Associate. “Our goal is to standardize the data to help organizations capture and report on utilization within a defined population of patients with diabetes. This effort will lay the groundwork for NCQA to develop a broader CGM performance measurement approach in the future.”

Learn more about recent updates to the Diabetes Recognition Program, or access the standards in the NCQA store.

Reducing the Administrative Burden of Utilization Management

NCQA continually evaluates its standards and programs to ensure they remain relevant and useful for the industry. This includes reducing administrative burden so organizations can focus on what matters most: providing high-quality, accessible care. Interoperability is the key to transforming cumbersome processes, like prior authorizations.

“In 2026, we will start to see the impact of the CMS Interoperability and Prior Authorization Final Rule, which should make the process less burdensome and more efficient,” says Tsveta Polhemus, NCQA’s Assistant Vice President, Product Management. “NCQA’s revised utilization management standards are tightly aligned with the federal rules, and extend beyond them, as we also include commercial plans. We provide guidance to help plans analyze denial and appeal rates to identify what’s working and what’s not so they can provide a better experience for clinicians and patients.”

Read our blog post, Breaking Down Silos in Utilization Management: A Data-Driven Approach, to learn more about the updates to NCQA’s utilization management standards.

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

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

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

The post NCQA’s 2026 Trends to Watch appeared first on NCQA.

Here’s a sample of our most popular self-paced courses. Complete them online any time, at your convenience.

HEDIS 101: Introduction to the Healthcare Effectiveness Data and Information Set

HEDIS is the most widely used system for measuring health care quality in the U.S. In this online, self-paced course, you’ll learn what HEDIS is, why it matters and how you can leverage its insights to drive better outcomes. This course is for quality improvement professionals who are new to HEDIS or want to deepen their understanding.

Introduction to the Digital Quality Transition

This online, self-paced course will provide a foundation in digital quality measurement: why it’s important, and how your organization can make a successful transition. This course is designed for quality and data reporting professionals working in health plans, health systems and technology organizations.

Health Plan Accreditation Survey Year 2026 Updates

This course includes seven self-paced modules covering updates, changes and policy clarifications for the Health Plan Accreditation standards and guidelines. It’s a great resource to help health plans, consultants, policymakers and vendors stay up to date on the latest requirements.

Accreditation in Utilization Management

This course provides a comprehensive explanation of updates and changes to NCQA’s Utilization Management Accreditation, including quality improvement for utilization management, collaboration with delegates, timeliness of decisions and more. NCQA surveyors and staff guide learners through requirement updates and their implications.

NCQA Essentials for Case Management

This course is designed for clinicians and health care professionals who are responsible for case management operations. It covers the fundamentals of case management—from systematic evidence reviews through care transitions—and takes a deep dive into the practical applications of case management, including working with interdisciplinary teams and helping to ensure that patients receive timely and appropriate care.

Introduction to PCMH 2026

This introductory course focuses on the fundamentals of the PCMH Recognition program, including the core elements and how to gather evidence to show compliance. Participants will also learn about policies, processes and procedures that help a practice transform into a medical home.

Advanced PCMH 2026

This advanced course takes a deeper dive into each PCMH concept and explores the characteristics of a successful medical home and the assessment process. It also focuses on advanced topics such as behavioral health integration, electronic clinical quality measures and how to receive automatic credit for certain standards. To get the most from this course, participants should have a working knowledge of the PCMH Recognition program.

Note: Save $395 when you bundle the Introduction to PCMH and Advanced PCMH courses.

Virtual Care Accreditation Essentials for PCMH Practices

This course will help participants from PCMH Recognized practices understand the value of NCQA’s Virtual Primary Care Accreditation, the similarities and differences between PCMH and Virtual Care standards and how to get started. Participants will also hear from a practice that achieved Virtual Care Accreditation.

PCMH Recognition Annual Reporting 2026: Continued Success

Learn about the goals of Annual Reporting and how to prepare for annual submission under PCMH Recognition. This course is designed for individuals working in PCMH practices who have at least one year of experience collecting, reporting and acting on annual reporting data.

Learn More

View our library of NCQA education courses here. To schedule training for a team, please contact us.

The post NCQA Education Courses: Use Your Training Budget Before Year’s End! appeared first on NCQA.

We could look at claims and clinical data like two “horses”: Well-prepared and accustomed to high performance standards—or potentially dangerous. Sometimes they can be a mix of the two as claims data might be better behaved than clinical data, and clinical data might be friskier and less predictable.

Success as a double rider depends on trusting two horses that have different qualities and personalities. The same applies when dealing with claims and clinical data. Trust is necessary—the trust that comes from having well-trained resources.

Data Quality Versus Data Governance

Attendees at the 2025 NCQA Health Innovation Summit know that data governance is a top priority in the health care quality community, even if it remains in the shadow of its more attractive relative, data quality. IBM defines data quality this way:

“Data quality measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness and fitness for purpose, and it is critical to all data governance initiatives within an organization.”

Data quality is essential to accurate and meaningful insights that can support decision making throughout health care. How is it related to data governance—AKA: the people, processes and technology involved in improving data quality? The American Health Information Management Association published this definition of data governance:

“The overall administration, through clearly defined procedures and plans, that assures the availability, integrity, security and usability of the structured and unstructured data available to an organization.” (AHIMA, 2020)

In other words, if you’re interested in improving data quality, you’re already doing some amount of data governance. Data quality should be an outcome of effective data governance.

Changing Our Attitude About Data Governance

NCQA Summit presenters encouraged us to think about quality as an “attitude”: a lived, highly observable part of an organization’s culture. If quality isn’t anchored in that culture, data quality efforts can fragment or fizzle out.

If we are to support AI, data from the Internet of Things, and the productization of Big Data, we will need good data achieved through governance. The problem is that data governance has a reputation for being a slow, painful, complicated, unfunded slog that is difficult to quantify and must be fully finished before data can be trusted or used.

It’s time we change that, and payers are in a key position to help.  What is a health plan’s role in governing upstream data? Simply stated, it is to help make data governance palatable. If we think about data governance as an attitude, rather than an overwhelming undertaking, we can achieve long-term effectiveness at improving data quality. And we know that data quality is essential to everything we hope to accomplish with data.

Payers can provide incentives, visible leadership and inspiration to encourage upstream data sources to develop a culture of data governance in their organizations and move toward sustainable data quality improvement at the source—to implement data governance in, across and between contributors to the data supply chain.

Tips for Advancing Data Governance

As new ways of using data multiply and accelerate, we all expect exciting and awe-inspiring feats of technology. To make great things happen, we must be able to trust data—data that has been governed. To that end, we suggest:

• Avoid the trap of thinking that data governance must be done perfectly across an organization before data can be used. Start by promoting the right attitudes, which sounds far less intimidating and much more actionable, and watch the action follow.
• Take credit for smaller-scale improvement as “data governance in action” for both payers and providers. Let’s turn the old idea of data governance as being “some kind of punishment” into a series of small wins to celebrate.
• Lead the way. HIEs and provider organizations need data targets. Payers can help by defining what data must be “fit for use” at the source and providing assistance and incentives to support strong data governance at the source. Payers can provide much-needed data governance leadership.

As we prepare for what promises to be an exciting year of AI and innovation, remember that all the shiny features we hope to see will stand on well-governed data that is the result of a combination of claims and clinical data. These are our primary horses—our sources!—to rely on. How will you inspire the provider community to train and govern that data for everyone to stand on?

This blog is brought to you by J2 Interactive and the views expressed are solely those of the sponsor.

The post Riding Two Horses at Once: Governing Data Quality in Both Claims and Clinical Data appeared first on NCQA.

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.

The post AI-Powered Data Transformation Accelerating Digital Quality appeared first on NCQA.

The shift to digital-first quality measurement is underway. To stay in compliance and competitive, organizations must start preparing now.

Traditional HEDIS^® relied on manual chart reviews and retrospective data collection, but this caused delays, inconsistencies and heavy administrative burden. NCQA is transitioning HEDIS reporting to FHIR^®-based digital quality measures that change the game, offering real-time reporting, greater accuracy and smarter insights for payers and providers. This is an opportunity to modernize infrastructure, streamline workflows and improve care quality.

The path forward can be complex. This blog offers a practical starting point for organizations ready to move beyond the basics and build a scalable, future-ready digital quality strategy.

The Eight FHIR Resources in Every Digital Quality Measure

To calculate quality measures digitally, you need structured, standardized data—and that starts with FHIR resources. Most digital measures rely on the same eight core FHIR resources. Prioritizing them during implementation simplifies adoption, ensures data readiness and helps align internal workflows for long-term interoperability.

1. The individual receiving care.
2. Interaction with the health care system (e.g., office visit).
3. Lab results, vital signs, screenings.
4. Clinical interventions (e.g., colorectal cancer screening).
5. Submitted charges for services.
6. Adjudicated claim outcomes.
7. Health insurance and plan information.
8. Results of the quality measure execution.

Five Practical Tips to Help You Get Started

For many organizations, the journey begins with a single question: Where do we start? Based on lessons from early adopters, here are five essential tips:

1. Engage stakeholders early. The shift to digital quality measures impacts multiple departments—from compliance and IT to clinicians and leadership. Early stakeholder involvement ensures shared goals, clarifies priorities and helps avoid misalignment between technical implementation and strategic outcomes.
2. Identify a cross-functional team. No single department can deliver digital quality. Build a team that blends clinical, data and technology expertise, and ensure collaboration across these groups to drive adoption.
3. Embrace testing and feedback. Start by validating how current data maps to required FHIR resources, and assess how well your systems support automation and logic execution. Then test, refine and incorporate feedback regularly before scaling to avoid large-scale rework down the line.
4. Start small, then scale. A focused pilot builds momentum and reduces risk. Learn quickly, adapt and use early wins to guide broader implementation.
5. Analyze and improve. Every step in the process offers valuable insights about data quality, internal readiness, tooling gaps and more. Build in time to review results, identify gaps, incorporate team feedback and refine a strategy before scaling.

What a Realistic Proof of Concept Looks Like

The first proof of concept (PoC) sets the stage for everything that follows.

Start with 1–2 measures. Select one or two digital quality measures that are clearly defined, well understood and relevant to organizational goals. This lets teams gain hands-on experience without being overwhelmed by complexity, and makes validation easier and faster.

Assess data readiness and gaps. Take stock of available data: where it lives, how complete it is, whether it’s structured in a way that aligns with FHIR resource requirements. Identify data gaps—missing fields, inconsistent coding, structural issues—early, so data can be cleaned up, completed or reorganized to meet FHIR requirements.

FHIR-enable legacy systems. No need to start from scratch! Consider extending existing infrastructure to expose the necessary data as FHIR resources using adapters, APIs or middle-layer tooling, where appropriate. This can help enable interoperability without major system overhauls.

Test for scalability. Once the PoC is operational, assess how easily it can be scaled. Can it support more measures, larger patient populations, additional teams? Evaluating performance while increasing demand helps determine whether an approach will work beyond the pilot phase.

Plan for integration. Think ahead about how measure outputs will be consumed. Will they feed into clinical dashboards, external regulators (e.g., NCQA) or performance management systems? A PoC should demonstrate not just data capture, but also how results will support real-world decisions across the organization.

Building for the Future

Digital quality measurement is no longer a future concept—it’s already underway. By taking clear, incremental steps now, organizations can stay ahead of compliance mandates while modernizing care delivery, reducing reporting burden and delivering real-time insights.

Getting started is about more than technology; it’s about building confidence, alignment and trust in a new way of working. Start small, scale smart and focus on the bigger picture: better care, better outcomes and a stronger foundation for value-based care.

This blog is brought to you by Firely 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 the registered trademarks of Health Level Seven International and their use does not constitute endorsement by HL7.

The post A Practical Guide to Getting Started With Digital Quality Measures appeared first on NCQA.

The Road To Digital Quality

Real-time Visual Notes created by Ink Factory.

Moderator David C. Kendrick of the OU-TU School of Community Medicine led a panel discussion of digital quality innovators—Bharat Sutariya of Oracle Health, Abdul Shaikh of Amazon Web Services and Anna Taylor of MultiCare Connected Care—who are mapping a course to deliver real value for patients.

Kendrick started by defining the problem: Data fragmentation. “A patient’s clinical data is scattered across six or more locations—the more complex a patient’s care is, the more places that data resides,” says

Kendrick. “This fragmentation exists not just within communities, but across state and national boundaries. So that’s a real risk for everything we’re trying to

accomplish in quality improvement.”

The move to digital quality measurement and interoperability can help to alleviate the problem of data fragmentation.

Building the Business Case for Digital Quality

Health care operates on a thin margin, so we need to optimize care delivery, patient experience and the cost of care. The return on investment for digital quality comes from investing in the data foundation. Moving to interoperable standards allows data to flow across organizations where it can be aggregated to create a more complete picture—and that applies to more than just quality measurement.

“If you can measure quality in your organization, I guarantee you are producing much higher insight operationally, financially and clinically,” says Sutariya. “When you have the infrastructure that supports quality, you can leverage that infrastructure for any business problem you’re trying to solve.”

Some organizations are already experiencing real financial impact from digital quality measures. “MultiCare took a really small use case, 30-day medication reconciliation, and started trading data with a payer. So now the payer knows how many gaps we’ve closed on a daily basis,” says Taylor. “It cost me about $15,000 to code and develop that application, and we earned about $17,000 for closing the care gaps. More importantly, we were able to contact the right patients to follow up on their care.”

Moving From Retrospective Measurement to Real-Time Insights

Digital measurement needs to go beyond the EHR—otherwise all we’ve accomplished is moving the data from a paper chart into an electronic one. The opportunity lies not in digitizing the quality metric, but integrating it upstream into the practice of medicine so practitioners know how they are doing in real time, as they are delivering care.

“This also ties in with personalized medicine,” says Shaikh. “The idea that you can have a real-time understanding of the patient with all the different types of data points and that it is accurate enough to drive a whole set of important outcomes.”

Assessing Data Quality in a Digital World

As we move toward more interoperable data formats and transmitting data through APIs, there are questions about data quality and integrity. As organizations map data to the FHIR® standard, they need quality checks on the mapping. For digital quality measures, organizations have the advantage of being able to compare results from traditional measures with digital ones.

“MultiCare reported our quality measures for Medicare shared savings this year using only FHIR data,” says Taylor. “We reported about 15,000 lives and I got to a standard deviation of 5% on my first try and I didn’t clean anything. So, the data quality and the mappings are pretty solid.”

Building Confidence in AI Through Standards

Real-time Visual Notes created by Ink Factory.

AI is transforming health care, but safe, responsible and reliable use requires more than good intentions, it demands standardized approaches that build trust. Vik Wadhwani, NCQA’s Chief Product and Transformation Officer, led a discussion with industry experts—Aaron Neinstein of Notable and Maia Hightower of Veritas Healthcare Insights—about how to build a trust layer across innovation, measurement and outcomes.

Defining Risk in AI Use

The perception of risk varies based on the stakeholder. Patients risk being hurt or having a poor outcome as a result of AI use. Clinicians may be concerned about the overall quality of care or the risk of malpractice if they follow an AI-recommended treatment. Many professionals in the health care system are also concerned about job loss. “What’s missing from the conversation, is the concern around automation bias,” says Hightower. “There is emerging evidence of overreliance on the recommendations of an AI system—and our youngest clinicians are at the greatest risk for this.”

How to Assess AI Tools

We should evaluate AI tools against existing health care system performance, rather than perfect outcomes. It’s easy to look at AI risk in a vacuum, but we have to compare it to how things are functioning today—to human performance. “Let’s be careful not to compare outcomes from AI to the health care system we wish we had, but to the health care system we actually have,” says Neinstein.

Build AI Governance Models Based on Risk Tolerance

Organizations have different levels of risk tolerance. Instead of trying to boil the ocean, be realistic and pragmatic about your current stage of AI governance. “If you’re a rock climber and you’ve got great harness, rope and helmet, you can climb high, far and fast. Think of that as your AI governance,” says Hightower. “But if you haven’t invested in that equipment, should you really be climbing way up to the cutting edge?”

Start With Low-Hanging Fruit

Rather than pursuing high-risk diagnostic AI, organizations should prioritize automating low-risk administrative tasks. “Is our biggest problem that we can’t diagnose cancer? No, the problem is that we can’t get the prior authorization or referral for a patient with cancer because the faxes are still sitting on a fax machine,” says Neinstein. “My advice is to go after the things that are high value and low risk, because there are a lot of them out there.”

Join Us Next Year

Mark your calendar for the 2026 Health Innovation Summit, October 4-7, in Atlanta, Georgia!

The post Doubling Down on Digital Quality and Evaluating the Risks of AI appeared first on NCQA.

The industry’s response has been predictable: Deploy more APIs, build better integrations, leverage AI. But although technology plays a crucial role, it’s only solving about one-tenth of the real problem. The fundamental issue isn’t only data integration, it’s also data quality. Health plans need more than “available” data; they also need to capture critical data elements for accurate quality measurement.

Data Quality Obstacles Health Plans Face

Information doesn’t always reside in one system in an organization. There might be multiple data silos across platforms—and even when an organization uses one EHR across locations, every clinician documents differently. Even data that come from the same EHR can contain different data elements and formatting.

In addition, performance-based contracts and quality measures evolve, changing the required data elements health plans need for reporting. Data quality issues are often identified too late. Health plans typically discover gaps during HEDIS^® reporting, when there’s no time for meaningful intervention.

The Data Quality Life Cycle: Beyond Basic Integration

Effective data quality requires multiple teams, working strategically: Engineers build tools for providers and internal reporting, expert mapping teams provide knowledge on various EHRs, clinical staff guide the coding and auditing processes.

The key is to build data quality checks into multiple phases of the process so each team can flag potential data issues and send them back to providers for correction. Here are some proven strategies for a comprehensive approach to improving data quality.

• Eliminate duplicate and inappropriate entries. Vendor collaboration can help fix common issues such as duplicate records, invalid entries and structural errors; for example, if a provider’s impression was mistakenly entered in the “Lab” field, making it appear that a lab test was completed when it wasn’t.
• Perform primary source verification. Organizations select 5–25 member visits—depending on clinic size—and review every status column, date field, description and value in the medical record. This can reveal workflow issues, such as when practices enter the date they received results, rather than the actual procedure date.
• Data validation. Data validation tools can categorize questionable information into warnings or hard errors. A warning might include data that should be reviewed, while a hard error highlights incorrect data—like a blood pressure reading of 5,000.
• Create a clinical data crosswalk. Many EHRs contain valuable clinical data that are improperly coded, but could be converted into a standardized format through a simple crosswalk. For example, using a numeric value for depression screening (e.g., PHQ-9), rather than a generic assessment.
• Leverage clinical expertise. EHRs were primarily designed for billing, not for comprehensive clinical documentation. Working with clinical experts who understand both the documentation process and clinical context can strengthen data quality improvement efforts.
• Educate providers. Successful programs frame conversations around education and support, rather than approaching care delivery organizations with complaints about missing data. Providers are invested in value-based arrangements, and are generally receptive to learning effective documentation practices.

Value of Quality Data for Health Plans

As the health care industry accelerates its shift toward value-based care, the consequences of poor data quality become more severe. Star ratings, HEDIS scores and risk adjustment accuracy all depend on reliable clinical data. More importantly, accurate data enable better clinical decisions, reduce duplicate testing and help get patients into care programs more quickly.

Health plans that focus on data quality, in addition to integration, see meaningful improvements—sometimes a half-point or more in Star ratings.

Building a Sustainable Data Quality Strategy

Data quality is an ongoing journey requiring clinical expertise, provider education and continuous monitoring. Thoughtful, comprehensive data quality programs that address the full life cycle of clinical information can be a competitive advantage that separates successful health plans from plans that struggle with the same data challenges year after year.

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

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

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Want to know who’s coming to the Summit? View the attendee list (by company name and title) here.

New! Poster Presentations

For the first time, the summit will feature a poster showcase—a spotlight on eight pioneering organizations that are improving health care quality. You’ll meet the leaders driving this critical work, and discover practical solutions to apply at your organization.

Poster presentation topics include:

• Advancing Behavioral Health Equity: Innovative Tools for Data-Informed Clinical Design
• Assessing Healthcare Disparities Using a Recently Developed Health Plan Quality Measures Index
• AI-Powered Predictive Interventions and Hyper-Personalization: A Revolution in Diabetes Care Management
• Data-Driven Strategies to Align Engagement, Results & ROI in Whole-Person Care for Vulnerable Groups
• Improved Transition of Care Guidance for Dually Eligible Populations

Don’t miss this unique opportunity to experience health care quality innovation up close. View all the poster presentations here.

Spotlight Presentations

Explore the future of health care quality innovation with spotlight presentations—dynamic, concise, power-packed talks happening in the Digital Innovation Theater.

Spotlight presentations include:

• Unlocking Quality Performance: Addressing Unmet Needs in Health Plan Care Management
• Lessons Learned on the Road to Digital Quality Reporting
• Trust Matters: Powering High-Quality, Validated Clinical Data
• Don’t Waste Another Day on Manual Gap Closure
• Quality in Sync: Closing Gaps and Boosting Performance With AI
• From Data to Equity: Powering Whole-Person Health Through AI and Integrated Care

View all the spotlight presentations here.

Meet the Experts

NCQA subject matter experts will be available for interactive discussions, offering a unique opportunity to gather insights about NCQA programs.

Join Us in San Diego!

Register today to spend three days with leaders in the quality ecosystem and engage with innovators transforming the health care industry. Book your room at the Gaylord Pacific Resort & Convention Center by September 19 to lock in the NCQA rate.

Groups of three or more enjoy special discounted pricing. Invest in your team’s growth while saving on registration. Please contact us for help with group registration.

The post Connect and Engage at the 2025 Health Innovation Summit appeared first on NCQA.

The summit is your opportunity to:

• Join conversations about today’s most pressing health care quality issues.
• Connect with subject matter experts, during and after sessions, for deeper learning.
• Take part in guided discussions that foster collaboration and actionable ideas.
• Network in the relaxed settings of the Opening Reception and Business Lounge.

Keynote Speakers at the Health Innovation Summit

This year’s summit features a powerhouse lineup of keynote speakers who are driving innovation and shaping the future of health care. Their sessions will dive into the most pressing and transformative topics in the industry.

Don’t miss this chance to hear from leading voices in health care quality.

Reinventing Quality in a New Era of Care

Find out how quality measurement will evolve to support a more proactive, sustainable health system—one that prioritizes prevention, improves chronic disease management and meets the complex needs of an aging population.

Keynote Speakers

• Dana Erickson, President and CEO, Blue Cross and Blue Shield of Minnesota
• Mark McClellan, MD, PhD, Director, Duke-Margolis Institute for Health Policy; Former CMS Administrator and FDA Commissioner
• Margaret O’Kane, President, NCQA
• Marc Overhage, MD, PhD, The Overhage Group; Member, NCQA Board of Directors

The Trust Layer: Building Confidence in AI Through Standards

This session brings together industry stakeholders to discuss the critical “enablement layer” of AI, and how to bridge innovation, measurement and accountability in AI-driven health care.

Keynote Speakers

• Maia Hightower, MD, MPH, MBA, CEO & Founder, Veritas Healthcare Insights; CEO & Founder, Equality AI
• Aaron Neinstein, MD, FAMIA, Chief Medical Officer, Notable Health
• Josh Wymer, Chief Health Information & Data Strategy Officer, State of Missouri Health Data Consortium

Disrupting Disparities: Playbooks for Change

Explore practical solutions for addressing health disparities through data collection, screening protocols and lessons learned. Discover how targeted strategies and real-world insights can drive meaningful improvement for all.

Keynote Speakers

• Danielle Brooks, JD, Corporate Director, Health Equity, AmeriHealth Caritas
• Donald Erwin, CEO, St. Thomas Community Health Center
• Tamara Thomas, National Vice President, Equity & Inclusion, Aledade

Setting the Bar: Quality and Impact in Behavioral Health Innovation

With rising behavioral health needs and ongoing workforce shortages, technology is helping bridge critical gaps. This discussion will explore how to uphold—and even elevate—standards of care in a rapidly evolving digital landscape.

Keynote Speakers

• Katherine Hobbs, MD, MPH, CEO, Author Health
• Kate McEvoy, Executive Director, National Association of Medicaid Directors
• Geoffrey Neimark, MD, Chief Medical Officer, CCBH

The Next Frontier in Caring for the Whole Person

This session explores innovative approaches that break down silos, foster collaboration and drive better outcomes for patients with complex health needs. Join experts as they discuss the future of quality and its role in enabling a holistic, person-centered approach to care.

Keynote Speakers

• Sachin H. Jain, MD, MBA, President & CEO, SCAN Health Plan
• Julie Murchinson, Partner, Transformation Capital

The Road To Digital Quality: Why It Is Worth the Drive

The road to digital quality in health care is full of detours, roadblocks and tricky intersections. This session will help you see the destination more clearly—and map a course to deliver real value for patients.

Keynote Speakers

• David Kendrick, Chief Executive Officer, MyHealth Access Network, Inc.
• Bharat Sutariya, Senior Vice President & Chief Health Officer, Oracle Health

View the entire list of sessions here.

Join Us in San Diego!

Spend three days with leaders in the quality ecosystem, and make valuable connections with innovators transforming the health care industry. Whether you’re attending for the first time or returning, you’ll leave energized—with new strategies, perspectives and partnerships to elevate your work.

Save $200+ if you register by September 5. Book your room at the Gaylord Pacific Resort & Convention Center by September 19 to lock in the NCQA rate.

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WISHIN is Wisconsin’s state-designated HIE. We spoke with Matt Gigot, WISHIN’s Director of Population Health and Analytics, about the importance of data quality, and how WISHIN is finding new ways to deploy clinical data to benefit the organizations they serve.

What types of services does WISHIN provide?

Gigot: WISHIN connects 2,200 care sites. The vast majority are hospitals and health systems, but we also work with other types of organizations like skilled nursing, long-term care facilities, health departments and pharmacies. We connect the EHRs between all participating organizations to make a consolidated, longitudinal patient record. Member organizations can use our portal to view the patient’s consolidated record. They can also subscribe to notifications when there are updates, such as an emergency department visit. Those types of notifications can support coordination of care.

How does WISHIN partner with health plans?

Gigot: Nearly all health plans in Wisconsin participate with us. While the health plans are mostly consumers of our data, they also submit data to us on a limited basis. The most common use case is for member care plans, which are mostly being exchanged for Medicaid populations. One of the primary benefits of participating with WISHIN is that we provide access to clinical data for their entire population, not just a sample. Having that data opens up opportunities for gap closure and patient follow-up, and enables coordination with care delivery organizations.

Why is data quality important for HEDIS reporting and other use cases?

Gigot: Data quality is critical because we are trying to accurately reflect what’s happening in the care setting—and that’s ultimately what HEDIS is trying to do as well. Consumers of the data want to feel confident that what we are sharing accurately represents the care experience of the patient.

How does WISHIN receive clinical data?

Gigot: We receive data in two ways. The first is HL7^® messages that are sent from the EHR in real time and may include non-standard codes to identify a service, such as a lab test or a colonoscopy. At some point after the data is entered into the EHR, it goes through a normalization process, where standard codes are applied (e.g., LOINC, SNOMED). That normalization is reflected in the second type of data we receive, a Consolidated Clinical Document Architecture (C-CDA) file. Those files are created at the close of a patient encounter, and they generally contain standard codes.

HL7 messages also have a description, so a user can understand what the service was, even if they don’t see a standard code. That is sufficient for many clinical applications like care management. But when it comes to HEDIS reporting, only the standard codes count, which is why the C-CDA is important.

What steps does WISHIN take to ensure data quality?

Gigot: We want to be confident in the provenance of the data, and make sure it reflects what’s in the medical record. Our team works with each customer during the onboarding process to make sure the data we expect to see in each field is actually coming in that way. Then we revisit that process whenever there are changes to the data model.

We haven’t encountered issues with degradation as a result of transferring data. Part of the reason is that we do not attempt to normalize the data, with the exception of some code mapping to standardize demographic fields, such as gender. The data we share reflects exactly what we receive from contributors.

Why did you decide to pursue NCQA’s Data Aggregator Validation?

Gigot: WISHIN was an early adopter, and we were validated in the first cohort. Getting validated was a way to demonstrate the depth and breadth of the data and provide value for our customers. NCQA validation is beneficial for our health plan customers because they can get clinical data from a single source, and that data has already been through primary source verification. It also benefits the health systems and hospitals because they don’t need to go through multiple data audits from the health plans. It establishes WISHIN as a trusted source of information, and demonstrates the rigorous processes we use to validate clinical data assets.

How can data quality assessments be improved or streamlined in the future?

Gigot: We are constantly looking for ways to leverage clinical data to support our customers’ needs. Participating in Data Aggregator Validation led us to create a Data Governance Committee that includes representation from our customers. We are leaning on this group to make recommendations and help us identify a path forward to improve data quality. By giving our customers a voice in the process, we’ve learned not to be prescriptive, and to allow ideas and solutions to bubble up organically.

A major focus is data standardization because customers are using clinical data for a multitude of purposes, and consistency of the codes is important. As part of this work, we are assessing the use of translation services, which would allow us to standardize pieces of information we receive that don’t currently have a standardized code. These services also often come with tools that would allow WISHIN and its customers to monitor data quality in real-time. Standardization enhances the usability of the information flowing through the WISHIN network, and opens the door for organizations to develop and implement tools that improve care for patients.

Learn More

• Read NCQA’s blog, Improving HEDIS Data Quality in a Digital World.
• Listen to our podcast, CQL and FHIR: Myths, Misunderstandings and More.
• Take our online education course, Introduction to the Digital Quality Transition.
• Find out how you can join NCQA’s Data Aggregator Validation program.

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