I recently joined 1upHealth’s Chief Product and Technology Officer Mohammad Jouni for a webinar on what digital quality measurement actually means for health plans — and why most organizations are thinking about it the wrong way.
Quality measurement is going digital. CMS and NCQA are moving HEDIS away from sample-based, retrospective reporting and toward full-population, FHIR-based digital quality measures. The hybrid method is retiring in measurement year 2029 with HEDIS becoming fully digital in measurement year 2030.Â
Most health plan teams are treating this as a reporting change. It’s not. It’s a data strategy decision, and the plans that recognize that early will spend the next few years building the right infrastructure instead of scrambling at the last moment.
What a Digital Quality Measure Actually Changes
A digital quality measure ships as executable code instead of a narrative specification. That one change removes the human interpretation layer that lets two plans run the same measure on the same members and arrive at different, equally defensible answers. When the logic is code, the result is consistent and auditable by design.
The other shift is in the data itself. Today, many health plans run electronic clinical quality measures (eCQMs) — measures built primarily around EHR data. Digital quality measures go further, pulling from claims, labs, registries, HIEs, and even a member’s previous payer. eCQMs are best understood as a subset of the broader dQM framework, not a synonym for it. A measure is only as good as the record it runs against, which is why the real work happens upstream of the measure.
Why Incomplete Clinical Data Is a Financial Problem, Not a Back-Office One
Missing and messy clinical data does not stay in the back office. It surfaces in Star Ratings, in contract withholds, and in the year-round cost of chart chasing that should have flowed digitally. A single drop in Star level can mean the loss of tens of millions of dollars for a Medicare Advantage plan, and the teams doing manual abstraction often describe it as a small army that never gets ahead.
Timing is the quiet killer. Claims lag, data sits in silos, and gaps surface too late to act on. Knowing about a care gap in February is actionable. Finding out in October, after the measurement year is effectively closed, is not. Digital quality measurement is valuable precisely because it shortens that distance between what happened and when you can see it.
FHIR Access Is Not the Same as a Complete Record
Mandated FHIR endpoints raise the floor, and that is real progress. But access is not the same as completeness. Providers use multiple EHRs, often one system for outpatient care and another for inpatient care. USCDI v3 defines a minimum content set, not everything quality measurement needs. And a vendor passing a compliance test does not guarantee that every site is documenting and exposing the data a measure depends on.
In other words, turning on FHIR APIs gets you connected. It doesn’t, on its own, hand you a complete longitudinal picture of the member. Closing that gap is the work that separates a connectivity project from a quality outcome.
Building a Golden Record for Each Patient
The answer is to consolidate every source into one normalized, FHIR-native longitudinal record, so measures run against a complete member picture rather than one system’s slice. That means transforming claims and other formats into FHIR, then resolving patient identities across sources with a mix of probabilistic and deterministic matching.Â
Records merge only at high confidence, and a human reviews the edge cases, because an incorrect merge exposes PHI and creates real HIPAA risk. We have seen this play out in practice. For one health plan, kept anonymous here, consolidating this way unified a population of about 85,000 members and brought over 350 clinical records per member into a single, measure-ready view.
When a member moves between plans, their prior record has critical insights into care that has previously been received as well as exclusions that may have been documented years prior. Payer-to-Payer Data Exchange, required under CMS-0057, lets a plan pull that history in and keep already-excluded members out of the measure in the first place, rather than discovering the error during an October chart chase.
How Health Plans Can Start Without Boiling the Ocean
The deciding factor is rarely the technology. It’s whether someone owns the data pipeline end to end. A practical sequence looks like this:
- Know your data first. Look at the last few measurement years, find where the issues actually are, and start with one or two well-understood measures.
- Prioritize by financial stakes. Build your first connectivity investments around the measures tied to your largest Star weights, withholds, and nearest transitions.
- Get provider connectivity right. Start with your highest-volume provider groups and meet them where they are.
- Build quality checks into the pipeline, not after it. When validation runs at ingestion, a stalled feed is caught in week two instead of week forty.
- Assign clear end-to-end ownership. Across IT, quality, and network management. The plans that struggle are the ones where nobody owns the whole problem.
Build the Infrastructure Once, Use It Everywhere
The same consolidated, clean, near-real-time data that powers HEDIS also powers care management, risk adjustment, prior authorization, network design, and member engagement. That’s the reason to treat digital quality not as next year’s compliance box, but as the springboard for population health you’ve been waiting for. Once the data finally lives in one place, the business problems you already have get easier to solve.
Want to go deeper? Watch the full webinar, now available on demand.
