ArticleJournal of the American Medical Informatics Association : JAMIA2020
Using UMLS for electronic health data standardization and database design.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Data Access, User Engagement, and Interoperability Standards in Public Health Data Portals: Environmental Scan Study.Online journal of public health informatics · 2026Article
- Validation of a mobile application for prehospital transport time data collection in Kigali, Rwanda.BMC global and public health · 2026Article
- Spotlighting the need for integrated health information technologies and databases to combat infections and sepsis in long-term care facilities.Innovation in aging · 2026Article
- The challenges and opportunities of continuous data quality improvement for healthcare administration data.JAMIA open · 2024Article
- Healthcare utilization and clinical characteristics of genetic epilepsy in electronic health records.Brain communications · 2024Article
- Natural language processing and expert follow-up establishes tachycardia association with CDKL5 deficiency disorder.Genetics in medicine open · 2024Article
- Clinical Data Warehousing: A Scoping Review.Journal of the Society for Clinical Data Management · 2024Article
- Nursing Informatics' Contribution to One Health.Yearbook of medical informatics · 2023Article
- A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study.Journal of medical Internet research · 2023Article
- A GCN-based approach to uncover misaligned synonymous terms in the UMLS Metathesaurus.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- High-risk diagnosis combinations in patients undergoing interhospital transfer: a retrospective observational study.BMC emergency medicine · 2022Observational
- ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials.BMC medical research methodology · 2022Article
- Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models.International journal of medical informatics · 2021Article
- The UMLS knowledge sources at 30: indispensable to current research and applications in biomedical informatics.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
objectivePatients 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.
methodsThe 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.
results431 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.
conclusionThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.