Computing eCQMs

Electronic Clinical Quality Measures (eCQMs) are computed over structured data
using CQL (Clinical Quality Language) logic against the QDM (Quality
Data Model). Much of what a measure needs — a counseling note, a reason a
service wasn't done, a symptom — lives only in free text. This skill uses
OpenMed to lift those facts out of notes (on-device) and feed them into measure
computation so numerators and valid exclusions aren't undercounted.

When to use this skill

When structured codes under-capture a measure population and the evidence is in
notes: documented exclusions ("patient declined screening"), numerator-relevant
findings, or symptoms gating a measure. Use it alongside a certified measure
engine — OpenMed supplements capture; it does not compute or certify the
measure.

eCQM anatomy (what you're populating)

Quick start

import openmed

note = (
    "Tobacco use screened today; patient is a current every-day smoker. "
    "Cessation counseling provided and cessation medication offered."
)

result = openmed.analyze_text(note, output_format="dict")
# entities -> {text, label, confidence, start, end}

# Lift two measure-relevant facts (illustrative, for a tobacco-screening eCQM):
facts = {
    "tobacco_status_documented": any(e["label"] in {"smoking_status", "tobacco_use"}
                                     for e in result["entities"]),
    "cessation_intervention_documented": "counseling" in note.lower(),
}
# These become QDM data elements your CQL references (see workflow).

Pick the model whose labels match the measure concept (choosing-openmed-models)
and code spans to value-set vocabularies via the linking skills before they
enter QDM.

Workflow

1. Read the measure. Get the human-readable spec + CQL + value sets from

ECQI / MADiE. Identify which populations depend on documentation that
structured data misses.

1. De-identify. Run openmed.deidentify on notes before any logging or

storage; keep the measure keyed by internal patient ids.

1. Extract facts. openmed.analyze_text for the concepts the measure needs

(status, intervention, reason-not-done). Use resolving-clinical-context to
drop negated/hypothetical/family-history mentions — a negated exclusion is
not an exclusion.

1. Code to value sets. Map entities to the codes the measure's value sets

expect (SNOMED/LOINC/RxNorm via the linking skills). QDM data elements are
defined by code membership, not raw strings.

1. Materialize QDM data elements. Turn coded, dated facts into QDM elements

(e.g. Assessment, Performed, Intervention, Performed,
Diagnosis) with the right author/relevant dates
(building-patient-timelines).

1. Compute with CQL. Feed the structured + note-derived QDM into a

certified CQL engine (e.g. the open-source cqframework engine). OpenMed
does not execute CQL.

1. Reconcile & audit. Track which population members were added by

note-derived facts and at what confidence, so QA can review.

Hand-off to / from OpenMed

• From OpenMed: analyze_text entities + clinical temporality + the

linking skills (to land facts in the measure's value sets) + deidentify
upstream.

• To measure tooling: materialized QDM data elements feed a CQL engine and

MADiE test decks. Note-derived QDM can also originate from etl-to-omop-cdm
rows if you compute measures on an OMOP store instead.

Edge cases & gotchas

• OpenMed supplements, it does not certify. Measure scoring must run in a

validated CQL engine. Treat note-derived facts as additional evidence subject
to review, not as authoritative measure results.

• Negation flips meaning. "Screening declined" is an exclusion; "screening

not declined" / "no contraindication" is the opposite. Always run the
temporality/negation pass before counting.

• Dates drive measurement periods. A fact only counts if its relevant date

falls in the measurement period. Resolve dates first; undated facts can't be
placed.

• Value-set membership, not keywords. A QDM data element is defined by codes

in the measure's value set. Map entities to those codes — don't match on the
surface word.

• No restricted terminology bundling. SNOMED/LOINC/RxNorm content stays

out-of-process under your own license; OpenMed provides spans/labels only.

• No raw PHI in logs or audit. Record measure provenance by offset, label,

confidence, and internal id.

Standards & references

• ECQI Resource Center (eCQM specs, CMS measures): https://ecqi.healthit.gov/
• CQL (Clinical Quality Language) v1.5 spec: https://cql.hl7.org/
• QDM (Quality Data Model) v5.6: https://ecqi.healthit.gov/qdm
• MADiE (Measure Authoring Development Integrated Environment):

https://madie.cms.gov/

• Open-source CQL engine (HL7 cqframework): https://github.com/cqframework/clinicalqualitylanguage
• OpenMed source: openmed/processing/ (analyze_text), openmed.clinical

(temporality).