Computing Ecqms

What it does

When your agent uses it

• The user wants to compute an eCQM
• Evaluate a CMS/ECQI quality measure
• Improve numerator capture from clinical notes
• Build CQL/QDM measure logic

Example prompts

• “/computing-ecqms”

Requirements

• Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

1. 1Read the measure. Get the human-readable spec + CQL + value sets from
2. 2De-identify. Run openmed.deidentify on notes before any logging or
3. 3Extract facts. openmed.analyze_text for the concepts the measure needs
4. 4Code to value sets. Map entities to the codes the measure's value sets
5. 5Materialize QDM data elements. Turn coded, dated facts into QDM elements
6. 6Compute with CQL. Feed the structured + note-derived QDM into a
7. 7Reconcile & audit. Track which population members were added by

What it can do on your machine

Read from SKILL.md and the folder at commit 252806a. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • ecqi.healthit.gov
  • cql.hl7.org
  • madie.cms.gov
  • github.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Computing Ecqms loads about 1.6k tokens when it runs. Until then it costs about 179 tokens; SKILL.md has 609 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~179

When it runs · the whole SKILL.md, loaded when a task matches

~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from maziyarpanahi/openmed at commit 252806a, republished under its Apache-2.0 licence (© maziyarpanahi). 609 words, ~1,593 tokens.

name

computing-ecqms

description

Compute electronic clinical quality measures (eCQMs) over structured data using CQL/QDM logic, lifting note-derived numerator and exclusion facts from OpenMed to improve measure capture. Use when the user wants to compute an eCQM, evaluate a CMS/ECQI quality measure, improve numerator capture from clinical notes, build CQL/QDM measure logic, or close documentation gaps that structured codes miss. Covers eCQM structure (IPP/denominator/numerator/exclusions), CQL v1.5 and QDM v5.6, MADiE authoring, and mapping OpenMed entities to QDM data elements. Consumes OpenMed analyze_text facts (coded via the linking skills) to supplement structured EHR data; does not replace certified measure engines.

license

Apache-2.0

metadata.project

OpenMed

metadata.category

analytics-reporting

metadata.pairs

after

metadata.version

1.0

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).

© maziyarpanahi, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/computing-ecqms of maziyarpanahi/openmed.

Open the folder on GitHubat commit 252806a