Using UMLS for electronic health data standardization and database design. (17th September 2020)

• Record Type: Journal Article
• Authors: Reimer, Andrew P; Milinovich, Alex
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 10(2020)
• Page Start: 1520
• Page End: 1528
• Publication Date: 2020-09-17
• Subjects: data management -- data curation -- electronic data processing -- data warehousing, transportation of patients ; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa176
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: 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.