The use of digital tools, combining software and hardware technologies to monitor patients remotely, is gaining traction,³ and is proving to be effective at not only managing therapy complications, but also at improving patients’ quality of life and satisfaction.² One such example is the use of Electronic Patient-Reported Outcomes (ePRO), digital tools that let patients report symptoms, side effects and quality-of-life indicators in real time. These tools have shown remarkable promise in oncology settings that can lead to better identification of patients’ needs, improving patient-provider communication, care management, and patient satisfaction, as well as decreased emergency department visits.⁴

At the federal level, in 2023 the Centers for Medicare & Medicaid Services (CMS) launched the Enhancing Oncology Model (EOM), a voluntary, value-based payment model covering Medicare beneficiaries. Lessons learned from a previous model, the Oncology Care Model, led CMS to require the use of ePROs by EOM⁴ participants beginning in performance period 5 (PP5), which corresponds to measurement year 3⁴, to monitor symptomatic toxicities, physical functioning, behavioral health and health-related social needs. Patients submit data via smartphones, tablets, or even voice response systems. The information is then integrated into EHRs, enabling care teams to respond quickly to concerning trends.^4,5

While large hospitals and health systems are often better equipped to implement remote monitoring and use of ePROs for patients, and have the ability to integrate care across specialties, community oncology practices—where most cancer patients get treatments⁶—face unique challenges to ensure integrated, coordinated care. For community oncologists, remote patient monitoring is a scalable way to extend care beyond the clinic. For rural or underserved populations, it bridges the gap between clinic visits and real-world needs, ensuring continuity of care. Data can be shared across teams, improving communication between oncologists, nurses and navigators, and patients report feeling more in control of their care and more connected to their providers.⁶

In a study of a large community oncology practice, the combination of ePRO and other remote monitoring tools demonstrated significant reductions in hospitalizations (39%), emergency department visits and overall cost of care ($1,146 per member per month in average savings per ePRO-monitored patient per month).⁷

The integration of ePROs and remote patient monitoring (RPM) into transitional oncology care represents a shift toward truly patient-centered models. These technologies can empower patients to be active participants in their treatment, reduce unnecessary utilization, enhance communication across providers and improve outcomes across the board.

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

¹ Patient Satisfaction and Quality of Surgical Care in U.S. Hospitals. Accessed at: https://pmc.ncbi.nlm.nih.gov/articles/PMC4248016/. September 15, 2025

² The association between care integration and care quality, Health Services Research. Volume 59, Issue 6, December 20204. Accessed at: https://onlinelibrary.wiley.com/doi/10.1111/1475-6773.14214. October 25, 2025.

³ Ochsner Chemotherapy Care Companion. Vol. 38, No.3, 2023. Accessed at: www.accc-cancer.org/docs/documents/oncology-issues/articles/2023/v38-n3/ochsner-chemotherapy-care-companion.pdf. September 16, 2025.

⁴ CMS Enhancing Oncology Model ePRO implementation Fact Sheet. Accessed at https://www.cms.gov/priorities/innovation/media/document/eom-epros-fs. September 15, 2025.

⁵ Reflections on the Oncology Care Model and Looking Ahead to the Enhancing Oncology Model. JCO Oncology Practice. Volume 18, No. 10. Accessed at: https://ascopubs.org/doi/10.1200/OP.22.00329#:~:text=One%20of%20the%20primary%20goals,requirements%20and%20drive%20quality%20improvement. September 16, 2025.

⁶ National Cancer Institute. Accessed at: https://www.cancer.gov/research/areas/disparities/chanita-hughes-halbert-clinical-trials-community-access#:~:text=Approximately%2085%25%20of%20cancer%20patients,the%20quality%20of%20their%20care. September 17, 2025

⁷ JCO Clinical Cancer Informatics. Impact of Remote Symptom Monitoring with Electronic Patient-Reported Outcomes on Hospitalization, Survival, and Cost in Community Oncology Practice: The Texas Two-Step Study. Patt, et. Al. Accessed at: https://ascopubs.org/doi/10.1200/CCI.23.00182. October 30, 2025.

The post Empowering Cancer Patients: How ePROs and Remote Monitoring Improve Transitions and Outcomes appeared first on NCQA.

In particular, clinical data must be integrated with operational and financial data—which has long been managed in separate silos—and with clinical data from other sources. Health care leaders who hope to fully unleash the power of clinical data to improve quality, care management and consumer experience must pursue a strategy that addresses the following considerations.

How is information from disparate data sets and streams accurately matched to individual members or patients?

Since the U.S. does not have universal patient identifiers, health care organizations must integrate claims and clinical data from multiple systems, with dueling formats for capturing patient information. This often leads to duplicate records and data gaps that can undercut quality and productivity. According to Black Book Research, 35% of all denied claims result from inaccurate patient identification or information.

An enterprise master person index should help overcome this challenge by drawing on an array of referential data, using both deterministic and probabilistic logic to match records and providing automated tuning tools to adjust algorithms for an organization’s unique needs.

How do legacy systems—from quality to care management to claims administration—talk to each other electronically?

Health care cannot afford to wait for industrywide adoption of FHIR^® to integrate clinical and administrative data. While FHIR is the right goal, we are in the early stages of this migration. The systems embedded in most organizations use multiple formats and standards; upgrading or swapping out these complex, costly systems will be a decade-plus endeavor. Additionally, there is variation in how FHIR standards are implemented in each organization and system, and the Implementation Guides published by the HL7^® Da Vinci Project continue to evolve.

To overcome this challenge, organizations’ data stacks must include a powerful data transformation engine that can support FHIR, HL7 v2, DICOM and IHE profiles; parse and pressure test incoming data feeds; and process high volumes of data in disparate standards and formats in real time.

How is information ingested from data streams for a specific purpose made readily available for multiple use cases?

Even successful data integration efforts too often focus on narrow use cases, forcing other departments and functions to recapture and normalize overlapping data elements. Silos are a lose-lose situation. Valuable information in data streams not relevant to an immediate use case is left on the cutting-room floor, while ancillary data that could have informed a critical decision is not considered.

To overcome this challenge, a comprehensive, health care-specific data model should anchor all components of a data integration strategy. The model must include fields to store all data elements relevant to the larger enterprise, in formats that can be easily utilized.

There is an enormous potential for data integration to transform care delivery. To achieve its promise, we must rethink long-standing data practices unique to our industry.

This blog is authored by InterSystems, and the views expressed are solely those of InterSystems.

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

The post Three Key Questions for Unlocking the Power of Clinical Data 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.

Real-time Visual Notes created by Ink Factory.

Thanks to everyone who attended this year’s Health Innovation Summit and made it a success! And if you couldn’t be there, keep reading for takeaways from the keynote session, Setting the Bar: Quality and Impact in Behavioral Health Innovation.

Moderator Geoffrey Neimark of Community Care Behavioral Health led a conversation with industry experts—Kate McEvoy of the National Association of Medicaid Directors and Michael Tang of Cityblock Health—about the risks and opportunities technology brings to behavioral health, especially for underserved populations.

Many factors contribute to the demand for behavioral health services: COVID pandemic isolation and social disconnection; overuse of alcohol, opioids and stimulants; the destigmatizing of behavioral conditions; increased behavioral health screening; the negative impact of social media. Unfortunately, the increase in demand coincides with a decrease in the number of practitioners.

“Behavioral health is primed to leverage technology to solve some of the access challenges,” says Neimark. “But there are also concerns about quality, equity and trust.”

Leveraging Technology in Behavioral Health

Telehealth is an effective way to improve access to behavioral health and substance use disorder care. Tang shared the story of a patient with schizophrenia who struggled to remember to take his pills every day and wanted to receive his medications through a shot. “I can supervise the patient virtually, even though I’m not in their home. Within minutes, I was on a virtual visit with him to assess his needs and discuss the medication,” says Tang.

McEvoy shared how Medicaid programs have embraced the use of technology to smooth the path to eligibility, improve access to services and supports through telehealth and enable better support and coordination of care for people with complex co-occurring needs.

“We recognize that different entities within state government—Medicaid, child welfare, departments of intellectual disability, the education system—all connect with children and families, but it’s been challenging to share data and coordinate efforts,” says McEvoy. “Interoperability can help identify needs and focus on those with the highest level of acuity.”

Risks of Using Technology and AI

Using AI with patients is a risk. “Many of our members come from minoritized communities and have distrust about new technologies. One of our core principles is that equity has to come first,” says Tang. “AI is part of care, but it’s not just AI by itself. It’s AI and humans working together to build trust and build relationships. We’re defining our AI governance processes and thinking cautiously about having AI work with our members.”

McEvoy also cautioned about the risk of relying too much on technology. “We’ve gone so far down the path with individualized home interventions, especially for older adults. We’re reexamining some programs to make sure we’re not contributing to social isolation, loneliness and depression.”

Technology and Health Literacy

While technology can provide many benefits, it’s not for everyone. Behavioral health organizations need to be thoughtful about how and where they implement technology, and consider limitations on broadband access or digital literacy.

“We’ve been surprised by the level of digital literacy of our members. People are texting and using their smartphones all the time,” says Tang. “But it is also situational. Sometimes our members can’t afford to pay their phone bill, so we need to be flexible and offer different ways for them to access services that are not technology dependent.”

Often, trust isn’t just about technology, it’s about the system itself. Having trusted messengers is also important. Community health workers or peer support specialists that share the same cultural background, speak the same language and have similar lived experience can build trust and, where appropriate, help facilitate the use of technology.

Evaluating the Use of Technology

Behavioral health organizations should consider these questions as they adopt new technologies:

• Who is the technology for—and who might it leave out?
• When is the technology solution appropriate—or inappropriate?
• Is the technology culturally responsive?
• Does the technology assume a certain level of digital literacy?
• How does the technology protect privacy and dignity?

“Technology cannot replace relationships. I’ve long been an advocate of group psychotherapy. It’s effective, it’s cost efficient, it amplifies access and it addresses social disconnection,” says Neimark. “I think we’re going to see a combination of growth in technology and growth in the human connection part of therapy.”

Join Us Next Year

Mark your calendar for the 2026 Health Innovation Summit, October 4–7, in Atlanta, Georgia. We hope to see you there!

The post Technology in Behavioral Health: Risks and Opportunities 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.

Picture this scenario: A woman with diabetes visits her doctor and receives a prescription. But once she’s home, she struggles. She doesn’t fully understand the instructions for taking the medication because English isn’t her first language, and she doesn’t know anyone who can help her translate. She also worries about being able to afford both the medication and groceries.

On paper, her care plan looks complete—in reality, it’s falling short. That’s why whole-person care matters.

Whole-Person Care Is More Than Responding to Symptoms

Health is shaped by a person’s mental health, daily stresses, cultural background, ability to communicate and more. Whole-person care recognizes these factors and addresses them alongside appropriate medical needs.

When providers account for social and behavioral health factors as well as medical conditions, care becomes more relevant. A care plan that includes counseling, access to food resources and language support is far more likely to help this woman than merely prescribing medication.

Building an Integrated Platform

Of course, care coordination doesn’t happen on its own; it requires an integrated platform that brings together patients, health care professionals and health plans. When information flows, patients don’t have to repeat their stories, and care teams can see the full picture.

An effective platform does more than store data. A truly effective platform:

• Tracks medical, behavioral and social needs in real time.
• Stores language preferences for all health plan members.
• Clearly and succinctly presents the most critical needs relevant to members.
• Sends reminders and prompts so important steps aren’t missed.
• Allows seamless hand-offs between clinicians.

Strong platforms generate insights from member interactions and care delivery, then evolve based on what they learn—that’s what makes them dynamic and valuable. Internal innovation becomes even more powerful when paired with the health plan partner perspective. Plans might see patterns across populations and bring a systemwide view of areas where members struggle. When platform intelligence and partner insights come together, the results are meaningful, providing lasting improvements that make care more connected and effective.

Omnichannel Outreach: Meeting People Where They Are

But even the best-designed platform won’t work if patients aren’t engaged. Omnichannel outreach can fill communication gaps that hinder the ability to provide meaningful care. By offering multiple ways to connect—phone call, text, email, secure messaging portal, letter, home visit—care teams can reach members in ways that fit their daily lives.

For some members, a quick text reminder is enough. For others, a call from a trusted coordinator makes the difference. For members with limited internet access, a mailed letter or in-person visit may be the most reliable touchpoint.

Respecting communication preferences also means honoring language needs. A message that arrives in a patient’s preferred language, or a call that connects them directly to bilingual staff, can prevent misunderstandings that put health at risk. Warm transfers—connecting a member immediately to the right resource—can build trust, reduce frustration and prevent a lost care opportunity.

Done well, omnichannel outreach prevents people from slipping through the cracks.

Working Together With Health Plans

Health plans are essential partners in whole-person care. They bring in resources that extend beyond clinic walls; for example, transportation services, SDOH solutions and care management programs.

Partnership helps close gaps quickly. A health plan might identify members who are overdue for preventive screenings. The care team, using omnichannel outreach, can contact a member through their preferred method of communication—and even schedule the appointment during the same interaction. When plans and providers align, member interactions are easier, and members receive more support.

Health plans also strengthen the feedback loop with insights that help technology evolve in ways that reflect real-world needs. Feedback shapes how integrated platforms grow to not only meet today’s needs, but to anticipate tomorrow’s challenges.

The Future Is Connected

If overall member health and health care delivery are key objectives, whole-person care is a necessary component. Care integration—linking people, processes and technology—creates a system where members feel supported across every dimension of health.

When the perspective of care shifts to become holistic, people can be more engaged, follow through on treatment and achieve better outcomes. Providers, health plans and patients can all benefit from a connected network. Ultimately, care integration is about treating the person, not just the symptoms. And when the system connects in ways that feel seamless and supportive, everyone wins.

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

The post Care Integration: Connecting People, Processes and Technology appeared first on NCQA.

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On the last day of the Health Innovation Summit in San Diego, NCQA President and Founder, Peggy O’Kane passed the torch to its next leader, Dr. Vivek Garg, marking 35 years of advancing health care quality and charting the course for NCQA’s future.

Here are some highlights from their conversation.

Dr. Garg: I feel like for the last few days I’ve been visiting the house that Peggy built. And it’s been awesome. This community is awesome, this movement you’ve helped to create. How do you feel about this community?

Peggy’ O’Kane: I just find it amazing to be in this role. This conference is usually really good, but this year I think it’s been particularly great. I feel incredibly fortunate to be working with the quality community, with our teams at NCQA, and now with you, Vivek.

Dr. Garg: I know you don’t like to talk about your achievements because you’re always focused on the future, but what are some of your proudest accomplishments?

Peggy O’Kane: We created the first national system of quality measurement. We created an accreditation program alongside employers who were the purchasers of health care. Those are very proud achievements. But the other thing I really feel proud about is that five years ago we decided that quality measurement isn’t working the way it should be. It should be much more embedded in medical practice. What we’re really trying to do with digital quality measurement is to have people look at their numbers and change the way care is delivered. Has it been a perfect journey? No. But have we gotten people’s attention with it? Yes, we have.

Dr. Garg: What do you think we should focus on next?

Peggy O’Kane: I think we need to enlarge the conversation. We are changing quality measures to be more salient, focused on more important issues and less burdensome. These things are going to take a while, but we also have to focus on the larger environment that these quality measures are deployed in. As long as we have such a fragmented delivery system, it’s really hard to identify who is an accountable entity. And when the accountable entity is a small medical practice, that’s pretty hard to measure. So, we need a policy environment that makes it in everybody’s interest to improve quality. We have a crazy reimbursement system in this country. It’s actively working in the wrong direction. We need to speak up and say, if you don’t pay primary care doctors appropriately, we cannot do this work. And if hospitals make more money by having readmissions than the penalty they pay, guess what’s going to happen?

Dr. Garg: Do you have any other advice for me, or the NCQA team, or this quality community?

Peggy O’Kane: We need to be much more intentionally working together. We’re all very busy in our own organizations, but we have a message to carry to the American public, to health policy leaders and to practitioners: We’re with you. We want to make this work.

Dr. Garg: That’s good advice. So, should we flip the table a little bit?

Peggy O’Kane: Yes. I want you to tell us about yourself and why you are coming to NCQA with this heartfelt dedication to quality.

Dr. Garg: Before I was a physician, I was a son. I grew up in a pretty small town in northern New Jersey. My father was a physician and I saw what being a clinician meant to him. My mother suffered from undiagnosed and unmanaged bipolar disorder for decades, and our family experienced a lot of social stigma, lack of understanding and emergent hospitalizations. I think we all have these experiences where we look at the health care journey and there’s so much lost opportunity. It doesn’t have to be that way. We have the science, the knowledge, the therapies, the interventions and the people. They’re just not set up in the right way. Coming to NCQA is an opportunity to take the quality measurement ecosystem forward, to put the insights in front of the people who actually work together to improve health.

Peggy O’Kane: You’ve had a big dose of NCQA for the past couple of days. What are the two or three things that you take away?

Dr. Garg: It’s clear that this community has a plan and a vision, and I want to be fully supportive of the great work that’s been started. As a clinician and someone working with clinical teams, I think patients assume the information is all there for their clinicians, and it’s not. I want to get to the point where producing the insights is not the work. The opportunity to help people improve their health begins at the point of having the insight. I think we can unleash a new wave of meaningful, clinically rich insights that help patients, families and clinicians see if conditions are optimally managed over time. That’s how we can bend the disease curve and the morbidity curve.

NCQA Announces the Quality Forward Fund

NCQA Board Chair, John Glaser joined O’Kane and Dr. Garg to announce the launch of the Quality Forward Fund—a collaborative effort to invest in research, consensus building and initiatives that drive better outcomes.

“This fund will target the areas where innovation is needed most,” says Glaser. “You’ve heard these themes throughout this conference: whole-person care, digital quality measurement and equitable access. It’s about putting resources where they’ll have the greatest impact on patients. In honor of Peggy’s enduring legacy, we’re inviting partners who share our mission to join us.”

Click here to learn more about the Quality Forward Fund.

Join Us Next Year

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

The post Passing the Quality Torch: Peggy O’Kane and Dr. Vivek Garg Share the Stage at the Health Innovation Summit 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.

Real-time Visual Notes created by Ink Factory.

As NCQA marks 35 years of advancing health care quality, the focus shifts from how far we’ve come to where we need to go next. What better place to have that conversation than the Health Innovation Summit, where industry leaders gather to discuss trending topics in health care.

NCQA Founder and President Peggy O’Kane moderated a powerhouse panel of visionary leaders—Mark McClellan of the Duke-Margolis Institute for Health Policy, J. Marc Overhage of The Overhage Group and Dana Erickson of Blue Cross Blue Shield of Minnesota—for a forward-looking discussion on the future of quality.

Challenges We Face in Reinventing Quality

The Medicare Advantage market is shrinking.

CMS projects that Medicare Advantage enrollment will fall by nearly 1 million members from 2025 to 2026—the first decline in decades. Large payers are exiting the market, leaving seniors searching for other coverage options.

Despite this trend, Erickson remains bullish on Medicare Advantage. “Beneficiaries like the experience because it provides care coordination and reduces the fragmentation that is so prevalent across our health care system,” says Erickson. “So, we believe it’s a good product and we still see a very bright future.”

Risk adjustment models are leaning towards ‘sick care’ and away from prevention.

McClellan explained that Version 28 of risk adjustment reform took out a lot of codes geared toward prevention and early stages of chronic disease that were being reported in Medicare Advantage plans. Those changes pushed us toward a fee-for-service infrastructure where downstream complications get coded more reliably than early interventions.

“Let’s not pay for the documentation, but for helping people see their risks and then change their risk trajectory,” says McClellan. “We can call it ‘paying for health.’ That’s what it really should be about.”

Technology creates opportunities, but setting priorities can be difficult.

In health care, we create programs for specific conditions, like heart disease or diabetes care, and we make progress. But the question is: How can we scale our efforts across a thousand different conditions and all of the other things we need to prioritize? That’s where we need to start thinking about systems and processes—and the data and technology that can enable large-scale change.

“Thirty years ago, the challenge was to get information into the clinician’s hands, and now we have care teams working in front of computer screens,” says Overhage. “We have data that is increasingly available and interoperable. We’re like 90% of the way there. I think in the next few years, with the right leadership and guidance, these systems will evolve in a way that makes care better for patients and makes our cost structure more sustainable.”

Value-based care has become a contested concept.

“Nobody knows what it is,” says Erickson. Because value-based care is so complex and has so many different definitions, it has been hard to implement—and that has led to disappointment within the industry. Until we reach a critical mass where we move enough of the financial incentive to pay for prevention and primary care, we’re going to get exactly what we’re paying for, which is acute care.

“Trying to do 5% of your operations one way while the other 95% is still being done another way just doesn’t work,” says Erickson. “You’re not changing the way you’re delivering care.”

Let’s Get This Train Moving

In closing, O’Kane asked each panelist to share the one thing we need to do right now to get the quality train moving in the right direction.

Alignment is key, says Overhage. “We need to continue to work on getting better alignment of the value proposition across the industry—that means patients, providers, health systems, health plans and the federal government—because the lack of alignment is going to continue to limit the progress that we can make.”

Erickson would double down on interoperability. “We have all of the technology, but we still haven’t done it right. We have to really commit to data interoperability—and that rising tide will lift all boats.”

McClellan would focus on getting data into the hands of patients. “I would like to see more groups embrace CMS efforts like ‘Kill the Clipboard’ and enable sharing of automated bulk FHIR^® data. We are already heading in that direction with prior authorizations.”

Join Us Next Year

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

The post Reinventing Quality in a New Era of Care: What Will It Take? appeared first on NCQA.