computing-ecqms

Extract eCQM evidence from clinical notes and map it to QDM data elements.

5.0k|615|Updated Oct 4, 2025
One-click install
npx skills add https://github.com/maziyarpanahi/openmed --skill computing-ecqms
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: computing-ecqms
Source: https://github.com/maziyarpanahi/openmed/tree/main/skills/computing-ecqms
Command: npx skills add https://github.com/maziyarpanahi/openmed --skill computing-ecqms

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps quality teams identify eCQM numerator, denominator exclusion, and exception evidence that structured EHR data may miss in clinical notes, while preserving the need for validated measure computation.

Core Features & Use Cases

  • eCQM Evidence Extraction: Use OpenMed to identify measure-relevant findings, interventions, statuses, and documented reasons for non-performance.
  • CQL and QDM Integration: Map note-derived facts to coded QDM data elements and incorporate them into certified CQL measure workflows.
  • Quality Gap Closure: Improve capture of counseling, screening decisions, symptoms, and valid exclusions that are absent from structured codes.
  • Audit and Safety Guidance: Address negation, temporality, value-set membership, de-identification, provenance, and confidence tracking.

Quick Start

Use the computing-ecqms skill to extract and validate measure-relevant facts from de-identified clinical notes, map them to the applicable value sets and QDM elements, and prepare them for computation in a certified CQL engine.

Frequently Asked Questions about computing-ecqms

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract eCQM numerator evidence from clinical notes?

To extract eCQM evidence from clinical notes, this Skill identifies measure-relevant findings, interventions, statuses, and documented reasons for non-performance that structured EHR data may miss.

Can I map clinical note findings to QDM data elements for CQL workflows?

Yes, note-derived facts are mapped to coded QDM data elements and incorporated into certified CQL measure workflows, satisfying CQL v1.5 and QDM v5.6 requirements for validated measure computation.

How do I handle denominator exclusions and exceptions in quality reporting?

Quality reporting requires capturing valid denominator exclusions and exceptions often absent from structured codes. This Skill identifies documented reasons for non-performance and symptoms in clinical notes to close quality gaps.

Does this approach support de-identification and provenance tracking for clinical quality measures?

Yes, the workflow addresses de-identification, provenance tracking, negation handling, clinical temporality, value-set membership, and confidence tracking to ensure audit-ready eCQM evidence extraction.

What is the best way to close quality gaps for CMS and ECQI measures using clinical notes?

Closing quality gaps for CMS and ECQI measures involves extracting measure-relevant evidence from clinical notes and mapping facts to QDM elements, preparing them for computation in a certified CQL engine.

Do I need a certified CQL engine to validate extracted eCQM data?

Yes, a certified CQL engine is required for validated measure computation. This Skill prepares note-derived facts by mapping them to applicable value sets and QDM elements for that engine.