ArticleJournal of the American Medical Informatics Association : JAMIA2013
Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.
What it found
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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
63 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.Journal of biomedical informatics · 2017Pooled it
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- Integrated disability evaluation in low back pain: feasibility and multidomain profiling from the EL DORADO cohort.Frontiers in rehabilitation sciences · 2026Article
- Review
- Clinical Research Informatics: a Decade-in-Review.Yearbook of medical informatics · 2024Review
- Developing a computable phenotype for glioblastoma.Neuro-oncology · 2024Article
- Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11.Ophthalmology science · 2023Article
- Research data warehouse: using electronic health records to conduct population-based observational studies.JAMIA open · 2023Article
- An Ontology-Based Approach for Consolidating Patient Data Standardized With European Norm/International Organization for Standardization 13606 (EN/ISO 13606) Into Joint Observational Medical Outcomes Partnership (OMOP) Repositories: Description of a Methodology.JMIR medical informatics · 2023Article
- Characterizing variability of electronic health record-driven phenotype definitions.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Design considerations for a hierarchical semantic compositional framework for medical natural language understanding.PloS one · 2023Article
- Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records.Methods of information in medicine · 2022Article
- An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning.Life (Basel, Switzerland) · 2022Article
- A natural language processing approach towards harmonisation of European medicinal product information.PloS one · 2022Article
- Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging.Frontiers in cardiovascular medicine · 2021Review
- Cohort Selection for Clinical Trials From Longitudinal Patient Records: Text Mining Approach.JMIR medical informatics · 2019Article
- Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries.BMC bioinformatics · 2019Article
- Classification, Ontology, and Precision Medicine.The New England journal of medicine · 2018Review
- The rendering of human phenotype and rare diseases in ICD-11.Journal of inherited metabolic disease · 2018Article
- Clinical Natural Language Processing in languages other than English: opportunities and challenges.Journal of biomedical semantics · 2018Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
32 authors.
Funding
Abstract
RESEARCH
objectiveTo develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. MATERIALS AND
methodsSoftware tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems-Mayo Clinic and Intermountain Healthcare-were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine.
resultsUsing CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria.
conclusionsEnd-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.
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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.