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Using UMLS for electronic health data standardization and database design.

Objective: Patients that undergo medical transfer represent 1 patient population that remains infrequently studied due to challenges in aggregating data across multiple domains and sources that are necessary to capture the entire episode of patient care. To facilitate access to and secondary use of...

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Published in:Journal of the American Medical Informatics Association Vol. 27; no. 10; pp. 1520 - 1529
Main Authors: Reimer, Andrew P, Milinovich, Alex
Format: Journal Article
Published: Oxford University Press / USA Oct2020
Online Access:View this record in EBSCOhost
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      jtl: Journal of the American Medical Informatics Association
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      dt: Oct2020
      vid: 27
      iid: 10
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      pub: Oxford University Press / USA
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        NLM32940707
        10.1093/jamia/ocaa176
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        146515051
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        atl: Using UMLS for electronic health data standardization and database design.
      aug:
        au:
          Reimer, Andrew P
          Milinovich, Alex
        affil: Frances Payne Bolton School of Nursing, Case Western Reserve University , Cleveland, Ohio, USA
      sug:
        subj:
          Transfer, Discharge
          Unified Medical Language System
          Resource Databases
      ab: Objective: Patients that undergo medical transfer represent 1 patient population that remains infrequently studied due to challenges in aggregating data across multiple domains and sources that are necessary to capture the entire episode of patient care. To facilitate access to and secondary use of transport patient data, we developed the Transport Data Repository that combines data from 3 separate domains and many sources within our health system.Methods: The repository is a relational database anchored by the Unified Medical Language System unique concept identifiers to integrate, map, and standardize the data into a common data model. Primary data domains included sending and receiving hospital encounters, medical transport record, and custom hospital transport log data. A 4-step mapping process was developed: 1) automatic source code match, 2) exact text match, 3) fuzzy matching, and 4) manual matching.Results: 431 090 total mappings were generated in the Transport Data Repository, consisting of 69 010 unique concepts with 77% of the data being mapped automatically. Transport Source Data yielded significantly lower mapping results with only 8% of data entities automatically mapped and a significant amount (43%) remaining unmapped.Discussion: The multistep mapping process resulted in a majority of data been automatically mapped. Poor matching of transport medical record data is due to the third-party vendor data being generated and stored in a nonstandardized format.Conclusion: The multistep mapping process developed and implemented is necessary to normalize electronic health data from multiple domains and sources into a common data model to support secondary use of data.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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