computing-ecqms

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).