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Int J Med Inform. 2010 Jul;79(7):515-22. doi: 10.1016/j.ijmedinf.2010.03.004. Epub 2010 Apr 24.

An analysis of clinical queries in an electronic health record search utility.

Author information

1
Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. karthik.natarajan@dbmi.columbia.edu

Abstract

PURPOSE:

While search engines have become nearly ubiquitous on the Web, electronic health records (EHRs) generally lack search functionality; furthermore, there is no knowledge on how and what healthcare providers search while using an EHR-based search utility. In this study, we sought to understand user needs as captured by their search queries.

METHODS:

This post-implementation study analyzed user search log files for 6 months from an EHR-based, free-text search utility at our large academic institution. The search logs were de-identified and then analyzed in two steps. First, two investigators classified all the unique queries as navigational, transactional, or informational searches. Second, three physician reviewers categorized a random sample of 357 informational searches into high-level semantic types derived from the Unified Medical Language System (UMLS). The reviewers were given overlapping data sets, such that two physicians reviewed each query.

RESULTS:

We analyzed 2207 queries performed by 436 unique users over a 6-month period. Of the 2207 queries, 980 were unique queries. Users of the search utility included clinicians, researchers and administrative staff. Across the whole user population, approximately 14.5% of the user searches were navigational searches and 85.1% were informational. Within informational searches, we found that users predominantly searched for laboratory results and specific diseases.

CONCLUSIONS:

A variety of user types, ranging from clinicians to administrative staff, took advantage of the EHR-based search utility. Though these users' search behavior differed, they predominantly performed informational searches related to laboratory results and specific diseases. Additionally, a number of queries were part of words, implying the need for a free-text module to be included in any future concept-based search algorithm.

PMID:
20418155
PMCID:
PMC2881186
DOI:
10.1016/j.ijmedinf.2010.03.004
[Indexed for MEDLINE]
Free PMC Article

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