A rationale for parsimonious laboratory term mapping by frequency.

Daniel Vreeman, John Finnell, J. Marc Overhage

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Mapping local observation codes to a standard vocabulary provides a bridge across the many islands of data that reside in isolated systems, but mapping is resource intensive. To help prioritize the mapping effort, we analyzed laboratory results reported over a thirteen month period from five institutions in the Indiana Network for Patient Care. Overall, more than 4,000 laboratory observation codes accounted for almost 49 million results. Of the observations reported in the thirteen months, 80 codes (2%) accounted for 80% of the total volume from all institutions and 784 codes (19%) accounted for 99% of the volume from all institutions. The 244 to 517 observation codes that represented 99% of the volume at each institution also captured all results for more than 99% of the patients at that institution. Our findings suggest that focusing the mapping effort on this modest set of high-yield codes can reduce the barriers to interoperability.

Original languageEnglish
Pages (from-to)771-775
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2007

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Observation
Vocabulary
Islands
Patient Care

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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title = "A rationale for parsimonious laboratory term mapping by frequency.",
abstract = "Mapping local observation codes to a standard vocabulary provides a bridge across the many islands of data that reside in isolated systems, but mapping is resource intensive. To help prioritize the mapping effort, we analyzed laboratory results reported over a thirteen month period from five institutions in the Indiana Network for Patient Care. Overall, more than 4,000 laboratory observation codes accounted for almost 49 million results. Of the observations reported in the thirteen months, 80 codes (2{\%}) accounted for 80{\%} of the total volume from all institutions and 784 codes (19{\%}) accounted for 99{\%} of the volume from all institutions. The 244 to 517 observation codes that represented 99{\%} of the volume at each institution also captured all results for more than 99{\%} of the patients at that institution. Our findings suggest that focusing the mapping effort on this modest set of high-yield codes can reduce the barriers to interoperability.",
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