Data platform · the foundation
The features don't matter if the data is wrong.
Ask a quality director why the last platform failed and the answer is rarely the features. The data never mapped correctly to the EHR and programs, the measures undercounted, and the team stopped trusting the numbers. Quaility starts there: every source unified into one identity-matched record, with the mapping proven measure by measure before you rely on a single dashboard.
The architecture
Every source lands in one record, and everything else runs on top
Sources land in a universal patient record built on an open healthcare data model. Measures, outreach, and analytics all compute on the same reconciled record.
Measure mapping validation
The difference between data that flows
and data that
actually counts.
Connecting a feed is the easy part. The part that sinks most platforms is making sure your EHR's codes actually register against each quality program's logic. When a code isn't mapped, the measure doesn't error. It just undercounts, and you find out at reporting time. Quaility checks every measure against your live data and tells you, up front, exactly where the mapping holds and where it needs a fix.
- Code-hit detection per program. For every measure, we test whether your EHR codes land against its numerator, denominator, and exclusion logic, or fall through.
- Gaps flagged, never hidden. An unmapped code or unlinked feed surfaces as “needs mapping,” with the measure and the missing volume named, instead of showing up as a quietly low rate.
- Mapping intervention, then re-validation. Our team maps the gap, and the same check confirms the events now land. Coverage is something you can see, measure by measure.
- So the numbers earn trust. You reach a record you believe, instead of admitting the tool is off and reconciling by hand forever.
Illustrative coverage view. Unmapped depression codes like these would have undercounted that measure by a third.
The record
20+ clinical entity types, one shape
Every source maps into the same open model, so a lab is a lab whether it came from your EHR, a claim, or a payer file.
Patients & demographics · Encounters · Conditions · Procedures · Labs & results · Medications · Pharmacy fills · Immunizations · Allergies · Vitals · Observations · Medical claims · Eligibility & coverage · Referrals · Appointments · Providers · Locations · Social history · Care-gap status · Outreach outcomes · …and more
Identity matching
One patient, no matter how many spellings
Probabilistic identity matching (EMPI) links the same person across your EHR, claims, and payer files, even when names, addresses, and IDs disagree. No duplicates, no double counting.
- Confidence thresholds. High-confidence matches link automatically; borderline ones never link silently.
- Human review queue. Judgment calls go to a person, with both records side by side.
- Reversible decisions. Every match decision is recorded and can be undone. Nothing links silently.
Where APIs end
No export button? We built one.
Some payer portals hold data you’re entitled to (care-gap rosters, quality reports) with no export and no API. Robotic process automation retrieves it on a schedule, the way a very patient staff member would.
- Scheduled, not heroic. Runs on a schedule instead of someone’s Friday afternoon downloads.
- Your data, your right. It only retrieves what your contracts already entitle you to see.
- Run history. Each run is recorded, so you can see what was retrieved and when.
Automation run · payer portal
Data quality
Your dashboards never quietly lie
The most dangerous data problem is the one nobody notices. Every pipeline run gets checked, and when something is off, your team hears about it before the dashboards mislead anyone.
Anomaly alerts
If patient counts spike, drop, or drift outside expected variance on a pipeline run, the platform flags it. A duplicate file or a truncated feed shows up as an alert, not as a mysteriously great quarter.
Pipeline freshness
Every feed carries a last-loaded timestamp, and stale feeds are surfaced explicitly. “When was this data refreshed?” has an answer on the page, not in a support ticket.
Checks on every run
Data-quality validation runs with every pipeline execution, not as a quarterly cleanup project. Problems get caught the night they happen, while the cause is still fresh.
Onboarding
From connected to trusted, not connected and resigned
The usual rollout: a long integration, a launch, and a slow realization that the numbers are off. So the team keeps a spreadsheet on the side and never quite trusts the platform. Quaility's onboarding makes coverage visible instead of asking you to take it on faith.
1 · Connect your sources
EHR, claims, eligibility, scheduling, and payer portals land in the universal record, including the feeds with no export, via robotic process automation.
2 · Validate the mapping
Every measure is tested against your live data. You get a coverage view of what's landing, what isn't, and the exact volume at stake, before anyone relies on a dashboard.
3 · Close the gaps
We map the unmapped codes and link the missing feeds, then re-run the same check until the measures you care about land, so silent undercounting doesn't carry into go-live.
4 · Go live on numbers you believe
Outreach, worklists, and reporting switch on over a record you've already seen validated. The platform earns trust on day one instead of losing it by month three.
Measures
39 quality measures, computed nightly on the unified record
Screenings, chronic disease control, behavioral health, maternal health, immunizations, and medication safety, with transparent numerator, denominator, and exclusion logic. Custom and state-specific measures run on the same engine.
- Breast cancer screening
- Cervical cancer screening
- Colorectal cancer screening
- Chlamydia screening
- Lead screening (children)
- Depression screening + follow-up
- Diabetes: HbA1c control
- Blood-pressure control
- Diabetic eye exams
- Kidney health evaluation
- Social needs screening
- Annual wellness visits
- Alcohol & substance use screening
- Adults’ access to preventive care
- Initial health assessment
- Well-child visits (0 to 15 mo)
- Well-child visits (15 to 30 mo)
- Well-child visits (3 to 21 y)
- Weight assessment & counseling
- Childhood immunizations
- Adolescent immunizations
- Topical fluoride
- Depression remission & response
- Follow-up after ED visit (mental illness)
- Follow-up after ED visit (substance use)
- Timely prenatal care
- Postpartum care
- Prenatal & postpartum depression screening
- Medication review (older adults)
- Functional status assessment
State program with an unusual denominator? Internal measure no vendor supports? Custom measures run on the same engine.
No black box
Built on an open-source healthcare data model
Your record lives in an open, documented model, not a proprietary schema you can’t inspect. You can see how every table is shaped, verify how every measure is computed, and export your data in a form other tools understand. If you ever leave, your data leaves with you.
Why the data layer is the difference
The federal data says it too: the EHR is not the ceiling.
We analyzed HRSA's 2024 quality data for all 1,510 federally funded health centers against the EHR vendor each one reports. The gaps between vendors are real, and the spread within every vendor is three times bigger. What separates the top decile is the layer on top of the EHR, not the EHR itself.
Source: HRSA UDS 2024, all Health Center Program awardees + look-alikes; medians, denominator ≥ 30. Full methodology, charts, and file hashes in the EHR Quality Gap report.
The platform in numbers
What the foundation carries
Measured at our first deployment, a Los Angeles community health center.
See it on your own data
Watch our AI call a patient.
Then imagine it calling
thousands of yours.
The demo takes 30 minutes. You'll see live AI outreach and your quality measures on a unified record, and we'll talk plainly about what we'd build for your workflows.