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Issues in creating and maintaining value sets for clinical quality measures.
To develop methods for assessing the validity, consistency and currency of value sets for clinical quality measures, in order to support the developers of quality measures in which such value sets are used.
We assessed the well-formedness of the codes (in a given code system), the existence and currency of the codes in the corresponding code system, using the UMLS and RxNorm terminology services. We also investigated the overlap among value sets using the Jaccard similarity measure.
We extracted 163,788 codes (76,062 unique codes) from 1463 unique value sets in the 113 quality measures published by the National Quality Forum (NQF) in December 2011. Overall, 5% of the codes are invalid (4% of the unique codes). We also found 67 duplicate value sets and 10 pairs of value sets exhibiting a high degree of similarity (Jaccard > .9).
Invalid codes affect a large proportion of the value sets (19%). 79% of the quality Measures have at least one value set exhibiting errors. However, 50% of the quality measures exhibit errors in less than 10 % of their value sets. The existence of duplicate and highly-similar value sets suggests the need for an authoritative repository of value sets and related tooling in order to support the development of quality measures.