Erin Heilman, SVP of Regulatory Affairs and Advisory Services at Medisolv, attended both HIMSS Global Health Conference & Exhibition and the CMS Quality Conference, where a consistent message emerged across sessions. In the following perspective, she breaks down the key themes shaping where quality measurement is headed next.
I recently attended both the HIMSS conference and the CMS Quality Conference, which happened back-to-back. CMS was present at HIMSS and of course leading their own quality conference. Across both events, there were some very consistent themes that came through, which I think they are highly relevant for quality leaders right now.
None of this is entirely new. CMS has been talking about interoperability for a long time, longer than they have been talking about digital measurement. And even digital measurement itself is not new.
But what felt different this time was this. It finally sounded like they believe this future is actually within reach.
Across CMS, ONC, and other speakers, there was a consistent message that with the technologies we now have, especially AI, the future they have been describing for years is closer than it has ever been. And across all of these sessions, the same themes kept coming up again and again.
If I had to summarize what we are being told about the future of quality measurement, it comes down to four ideas. Measurement is becoming more interoperable, more digital, more longitudinal, and more actionable.
Those four themes showed up everywhere.
Interoperability was, as expected, a major focus. One speaker joked that she had been giving the same interoperability talk for the last ten years, but this time it felt different. There was a strong sense that AI is shifting the conversation from something that might be possible to something that is actually achievable.
At the end of the day, this all comes down to data. For quality leaders, this is foundational to your future measurement requirements. Measurement is going to depend on whether you can gather and trust data across all of the places it lives today. Historically, quality leaders have relied on claims data and manually abstracted data, and more recently on EHR data. But even within those environments, it is still difficult to get a complete, clean view of the patient. The future assumes that problem is solved, or at least significantly improved.
One word I heard repeatedly, which I do not think I have ever heard used from the stage before, was “multimodal.” It came up in almost every session.
Multimodal is really just a more sophisticated way of saying we need to bring together data from all the places it exists for a patient. Not just claims data, and not just EHR data, but also labs, pharmacy, devices, and increasingly consumer-generated data like wearables.
The vision is that we move toward a complete picture of the patient that pulls from all of these sources. And importantly, this is not meant to be done by people. The expectation is that AI agents and federated workflows will pull this data together across fragmented systems into something usable.
That is the groundwork. Interoperability is about creating a clear, complete, and usable data picture.
As a quality leader, you should be thinking about what constitutes a complete data picture for the patients your organization serves.
Where is that data and do you trust it?
The second major theme was digital quality measurement. There was a lot of discussion about digital quality measures, or dQMs. In the way it was described at these conferences, dQMs are the next evolution of eCQMs, but built on a different technical foundation. They rely on standardized digital data that can be pulled from multiple sources and computed in a more automated way. Most of what CMS is moving toward is FHIR-based, which is why that term came up so often.
FHIR can mean a couple of things, and this is where some of the confusion comes in. It refers both to how data is structured and to how it is accessed. On the one hand, it defines standardized data elements and models, which align to efforts like USCDI+. On the other hand, it enables that data to be retrieved through APIs.
In the context of dQMs, it is really the combination of those two things that matters. It is not just a new way to transport data, and it is not just a new data model. It is a way to make quality measurement computable, using standardized data that can be accessed directly from systems rather than manually abstracted.
CMS announced earlier this year that hospital eCQMs have now been specified as dQMs, meaning they are available in this FHIR-based format. That is a significant step forward.
At the same time, they were very clear that this is not going to be an overnight transition. What we should expect is a phased approach. There will likely be pilots, then a period where organizations can choose between current eCQM submission methods and FHIR-based submission, and eventually a full transition.
So while there is a lot of momentum, this is still going to take time.
What is more important than the mechanics of FHIR is the vision behind it.
CMS is trying to move to a world where measurement is fully digital. That means no manual abstraction. No human review of charts to pull data into measures. And eventually, no real concept of “submission” as we think about it today.
The idea is that once the data infrastructure is in place, measures can be generated automatically. Data flows from systems, measures calculate continuously, and reporting becomes almost a byproduct rather than a separate activity.
It is a compelling vision. But it depends entirely on having the right data, in the right format, available at the right time. Which again brings us back to interoperability.
The third theme, and in many ways the most important shift conceptually, is the move from snapshots to longitudinal measurement.
Today’s measures are largely retrospective. They look at whether something happened during a measurement period and report that months after the fact. They served an important purpose. They created transparency and accountability, and they did drive improvement.
But there was a strong message that they are not enough anymore.
Dr. Harlan Krumholz, Founder, Center for Outcomes Research and Evaluation (CORE), spoke about this directly. His argument was that current measures are too slow, too costly, and not actionable. They tell you what happened, but they do not help clinicians or organizations understand what to do next.
He also made the point that risk adjustment, as we use it today, is too crude. It reduces complex patients into static categories based on diagnosis codes. It misses important differences between patients and between organizations. And others at the conference added that it has also created incentives to optimize coding rather than necessarily improving care.
The direction he outlined, and that others reinforced, is toward measurement that reflects the patient over time. Not just a single data point, but a trajectory. So instead of asking, “Was BP controlled at the last visit?” we will ask, “What percentage of days over the past 6–12 months was the patient’s blood pressure within target range?”
The future of measurement is intended to capture longitudinal patterns to understand how a patient’s condition is evolving. And to use that information to guide care in real time.
This is where the idea of actionable measurement comes in.
If interoperability gives us the data, and digital measures give us the infrastructure, then the goal is to produce measures that actually inform decisions. Not months later, but at the point of care or close to it.
The vision is that measures will not just support accountability and improvement after the fact, but will also support real-time decision making and even discovery. Patterns across patients can be identified, and those insights can be fed back into care.
Underlying all of this is the assumption that data will be passively collected, not manually abstracted. And that measurement will become more precise. Patients are not interchangeable, and the expectation is that measurement will increasingly reflect that.
There was also a clear connection to patient empowerment. The idea that these same measurement systems should not only serve health systems and regulators, but also patients themselves. Patients should be able to understand how their care aligns with evidence, how their condition is evolving, and what options they have.
AI plays a role across all of this, but not as a standalone concept. It was consistently positioned as an enabler. Its role is to make the data usable, to scale analysis, to support triage, navigation, and patient engagement. Not AI for its own sake, but AI as the mechanism that makes this entire model work.
So when you put all of this together, the message coming from both HIMSS and the CMS Quality Conference is fairly clear.
The future of quality measurement is not going to look like retrospective reporting built on manual processes. It is moving toward a model that is interoperable, digital, longitudinal, and actionable.
It will rely on data from across the ecosystem, not just within the four walls of the hospital or a single EHR. It will operate closer to real time. And it will increasingly be tied to value-based care models, where quality and cost together determine success.
For quality leaders, that has some immediate implications.
You need to understand whether your organization can assemble trustworthy, reusable data across settings and sources. You need to be paying attention to FHIR-based dQM pilots as they emerge. You should expect a transition period, not an immediate shift, but you should also expect that this is the direction CMS is going.
And perhaps most importantly, you need to start thinking about quality not just as a reporting function, but as a capability that is embedded in care delivery.
Because the long-term shift is this. We are moving from measuring whether something happened to understanding whether care is actually working for a patient over time.
That is a very different model than the one we have today.
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