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Items: 5

1.
Figure 4

Figure 4. From: Supporting Information Retrieval from Electronic Health Records: A Report of University of Michigan's Nine-Year Experience in Developing and Using the Electronic Medical Record Search Engine (EMERSE).

The document Summaries view where the rows represent each of the documents belonging to a specific patient. Documents that do not contain any of the search terms are also included for viewing if needed.

David A. Hanauer, et al. J Biomed Inform. ;55:290-300.
2.
Figure 3

Figure 3. From: Supporting Information Retrieval from Electronic Health Records: A Report of University of Michigan's Nine-Year Experience in Developing and Using the Electronic Medical Record Search Engine (EMERSE).

The Overview screen, presenting the search results for eight terms related to animal bites and wound cleaning. Each row on the Overview screen represents a patient and each column represents a document source. All patient identifiers have been replaced with realistic substitutes.

David A. Hanauer, et al. J Biomed Inform. ;55:290-300.
3.
Figure 2

Figure 2. From: Supporting Information Retrieval from Electronic Health Records: A Report of University of Michigan's Nine-Year Experience in Developing and Using the Electronic Medical Record Search Engine (EMERSE).

Screen capture of EMERSE showing the editing of a search terms bundle. In the example, the term “cefuroxime” was added by the user and the system provided additional suggestions. To aid in recognizing terms in the search results, the Bundles feature will highlight synonyms and related concepts using the same color, which can be overridden by users.

David A. Hanauer, et al. J Biomed Inform. ;55:290-300.
4.
Figure 1

Figure 1. From: Supporting Information Retrieval from Electronic Health Records: A Report of University of Michigan's Nine-Year Experience in Developing and Using the Electronic Medical Record Search Engine (EMERSE).

EMERSE main workspace. Screen capture of EMERSE showing where search terms can be entered. Search keywords can be quickly typed into the Quick Search text box. Available pre-saved collections of search terms (i.e., search term bundles) are listed further down on the same page. In this example, a search term bundle named “Animal bites” has been selected and the terms belonging to the bundle are shown at the top.

David A. Hanauer, et al. J Biomed Inform. ;55:290-300.
5.
Figure 5

Figure 5. From: Supporting Information Retrieval from Electronic Health Records: A Report of University of Michigan's Nine-Year Experience in Developing and Using the Electronic Medical Record Search Engine (EMERSE).

The Technical Architecture of EMERSE. Data are captured from the sources of EHR data (right side) mainly via HL7 interfaces and added to locally-maintained document repositories separate from the EHR. The Indexing services server routinely updates the Lucene-based indices used by the main search application from these databases. Note that while having all documents indexed in both a case-sensitive and case-insensitive index increases storage requirements, this approach allows for faster response time for most search scenarios.

David A. Hanauer, et al. J Biomed Inform. ;55:290-300.

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