ArticleJournal of the American Medical Informatics Association : JAMIA2016
Data interchange using i2b2.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Common data models and data standards for tabular health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- A proposed organization of multifaceted knowledge in data repositories: structuring record collections and evidence sets for data lakehouses and moonshots.Journal of exposure science & environmental epidemiology · 2026Review
- Article
- Impact of State Policies on Opioid Prescribing Among Surgery and Injury Patients: Controlled Interrupted Time-Series Study, North Carolina, 2014-2019.Pharmacoepidemiology and drug safety · 2025Article
- Development and Validation of Natural Language Processing Algorithms in the ENACT National Electronic Health Record Research Network.medRxiv : the preprint server for health sciences · 2025Article
- Clinical Research Informatics: a Decade-in-Review.Yearbook of medical informatics · 2024Review
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review.JMIR medical informatics · 2024Article
- Alpha-1-antitrypsin-deficiency is associated with lower cardiovascular risk: an approach based on federated learning.Respiratory research · 2024Article
- Technologies to Study Genetics and Molecular Pathways.Advances in experimental medicine and biology · 2024Review
- A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Data Quality- and Utility-Compliant Anonymization of Common Data Model-Harmonized Electronic Health Record Data: Protocol for a Scoping Review.JMIR research protocols · 2023Article
- Representing and utilizing clinical textual data for real world studies: An OHDSI approach.Journal of biomedical 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
- Technology Platforms and Approaches for Building and Evaluating Machine Learning Methods in Healthcare.The journal of applied laboratory medicine · 2023Article
- How data science and AI-based technologies impact genomics.Singapore medical journal · 2023Review
- Ten Years of Medical Informatics and Standards Support for Clinical Research in an Infectious Diseases Network.Applied clinical informatics · 2023Article
- Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study.JMIR medical informatics · 2022Article
- From Real-world Individuals' Data to National Health Indicators: Multiphase Pilot Study in Gabon.JMIR formative research · 2022Article
- Developing an ETL tool for converting the PCORnet CDM into the OMOP CDM to facilitate the COVID-19 data integration.Journal of biomedical informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectiveReinventing data extraction from electronic health records (EHRs) to meet new analytical needs is slow and expensive. However, each new data research network that wishes to support its own analytics tends to develop its own data model. Joining these different networks without new data extraction, transform, and load (ETL) processes can reduce the time and expense needed to participate. The Informatics for Integrating Biology and the Bedside (i2b2) project supports data network interoperability through an ontology-driven approach. We use i2b2 as a hub, to rapidly reconfigure data to meet new analytical requirements without new ETL programming. MATERIALS AND
methodsOur 12-site National Patient-Centered Clinical Research Network (PCORnet) Clinical Data Research Network (CDRN) uses i2b2 to query data. We developed a process to generate a PCORnet Common Data Model (CDM) physical database directly from existing i2b2 systems, thereby supporting PCORnet analytic queries without new ETL programming. This involved: a formalized process for representing i2b2 information models (the specification of data types and formats); an information model that represents CDM Version 1.0; and a program that generates CDM tables, driven by this information model. This approach is generalizable to any logical information model.
resultsEight PCORnet CDRN sites have implemented this approach and generated a CDM database without a new ETL process from the EHR. This enables federated querying within the CDRN and compatibility with the national PCORnet Distributed Research Network. DISCUSSION: We have established a way to adapt i2b2 to new information models without requiring changes to the underlying data. Eight Scalable Collaborative Infrastructure for a Learning Health System sites vetted this methodology, resulting in a network that, at present, supports research on 10 million patients' data.
conclusionNew analytical requirements can be quickly and cost-effectively supported by i2b2 without creating new data extraction processes from the EHR.
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.