computing-ecqms
将文本证据转为可计算事实，提升度量的覆盖率与准确性。
eCQM 输出依赖数据映射与质量，需在生产前进行验证与校准。
使用 CQL 基于结构化数据和 OpenMed 抽取事实计算电子临床质量度量（eCQM）。
把结构化病历和笔记里能找到的事实拼起来算出电子临床质量度量（eCQM），用 CQL/QDM 的逻辑跑出 IPP/分母/分子/排除等项。特别适合在要评估 CMS/ECQI 指标、写或调试 CQL 逻辑、或弥补结构化编码漏记时使用。会把 OpenMed 从自由文本里抽出来的分子/排除事实映射到 QDM 数据元，补充 EHR 里缺的证据，但并不替代认证的测量引擎。
▸ 展开 SKILL.md 英文原文
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.
帮我安装这个 skill：https://raw.githubusercontent.com/maziyarpanahi/openmed/master/skills/computing-ecqms/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/maziyarpanahi/openmed/master/skills/computing-ecqms/SKILL.md"# 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 measur