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Updated: May 15, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Issues in creating and maintaining value sets for clinical quality measures.

Rainer Winnenburg¹, Olivier Bodenreider

¹National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA. winnenburgr@mail.nih.gov

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|January 11, 2013

Summary

Invalid codes and duplicate value sets are common in clinical quality measures, impacting many measures. Developing a central repository and tools is crucial for improving value set quality and supporting measure developers.

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Last Updated: May 15, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Area of Science:

Health Informatics
Clinical Quality Measurement
Terminology Services

Background:

Value sets are essential components of clinical quality measures.
Ensuring the validity, consistency, and currency of value sets is critical for accurate quality assessment.
Current methods for value set assessment are insufficient.

Purpose of the Study:

To develop and apply methods for assessing the validity, consistency, and currency of value sets used in clinical quality measures.
To support developers of clinical quality measures by identifying issues in their value sets.

Main Methods:

Utilized the Unified Medical Language System (UMLS) and RxNorm terminology services to assess code well-formedness, existence, and currency.
Employed the Jaccard similarity measure to investigate overlap and identify duplicate or highly similar value sets.

Main Results:

Analyzed 1463 unique value sets from 113 National Quality Forum (NQF) measures, extracting over 163,000 codes.
Found 5% of all codes and 4% of unique codes to be invalid.
Identified 67 duplicate value sets and 10 pairs with high similarity (Jaccard > 0.9).

Conclusions:

A significant proportion of value sets (19%) contain invalid codes, affecting 79% of quality measures.
Duplicate and highly similar value sets indicate a need for a centralized repository and improved tooling.
Addressing these issues is vital for enhancing the reliability and utility of clinical quality measures.