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J Am Med Inform Assoc. 2014 Mar-Apr;21(2):285-91. doi: 10.1136/amiajnl-2013-001921. Epub 2013 Nov 5.

Formalization and computation of quality measures based on electronic medical records.

Author information

1
Department of Computer Science, VU University Amsterdam, Amsterdam, The Netherlands.

Abstract

OBJECTIVE:

Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable--that is, applicable to a large set of heterogeneous measures of different types and from various domains.

MATERIALS AND METHODS:

We formalized the entire set of 159 Dutch quality measures for general practice, which contains structure, process, and outcome measures and covers seven domains. We relied on a web-based tool to facilitate the application of our method. Subsequently, we computed the measures on the basis of a large database of real patient data.

RESULTS:

Our CLIF method enabled us to fully formalize 100% of the measures. Owing to missing functionality, the accompanying tool could support full formalization of only 86% of the quality measures into Structured Query Language (SQL) queries. The remaining 14% of the measures required manual application of our CLIF method by directly translating the respective criteria into SQL. The results obtained by computing the measures show a strong correlation with results computed independently by two other parties.

CONCLUSIONS:

The CLIF method covers all quality measures after having been extended by an additional step. Our web tool requires further refinement for CLIF to be applied completely automatically. We therefore conclude that CLIF is sufficiently generalizable to be able to formalize the entire set of Dutch quality measures for general practice.

KEYWORDS:

EMR-driven Phenotyping; Electronic Medical Record; Identification of Patient Cohorts; Quality Indicators; Quality Measures; Secondary Use of Patient Data

PMID:
24192317
PMCID:
PMC3932459
DOI:
10.1136/amiajnl-2013-001921
[Indexed for MEDLINE]
Free PMC Article

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