ArticleBMC medical informatics and decision making2017
Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 65 citations in OpenAlex.
- Article
- Toward bidirectional FHIR-OMOP CDM transformations using TermX to support the secondary use of real-world health data within a patient-centered digital health paradigm.Frontiers in medicine · 2026Article
- I-ETL: an interoperability-aware health (meta)data pipeline to enable federated analyses.BMC medical informatics and decision making · 2025Article
- Streamlining electronic medical record data extraction and validation in digital hospitals: A systematic review to identify optimal approaches and methods.Learning health systems · 2025Article
- A software pipeline for medical information extraction with large language models, open source and suitable for oncology.NPJ precision oncology · 2025Review
- Lessons Learned From Building a Data Platform for Longitudinal, Analytical Use Cases and Scaling to 77 German Hospitals: Implementation Report.JMIR medical informatics · 2025Article
- Enhancing Gen3 for clinical trial time series analytics and data discovery: a data commons framework for NIH clinical trials.Frontiers in digital health · 2025Article
- Discrepancies in Aggregate Patient Data between Two Sources with Data Originating from the Same Electronic Health Record: A Case Study.Applied clinical informatics · 2025Article
- Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System.Frontiers in digital health · 2025Article
- A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation.JMIR medical informatics · 2024Article
- LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models.medRxiv : the preprint server for health sciences · 2024Article
- Anesthesia decision analysis using a cloud-based big data platform.European journal of medical research · 2024Review
- Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review.JMIR medical informatics · 2024Article
- Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record.The American journal of cardiology · 2023Review
- Engaging Patients and Other Stakeholders in "Designing for Dissemination" of Record Linkage Methods and Tools.Applied clinical informatics · 2023Article
- TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse.Methods of information in medicine · 2022Article
- Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project.Frontiers in digital health · 2022Article
- The Collaborative Metadata Repository (CoMetaR) Web App: Quantitative and Qualitative Usability Evaluation.JMIR medical informatics · 2021Article
- Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- The Generalized Data Model for clinical research.BMC medical informatics and decision making · 2019Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundElectronic health records (EHRs) contain detailed clinical data stored in proprietary formats with non-standard codes and structures. Participating in multi-site clinical research networks requires EHR data to be restructured and transformed into a common format and standard terminologies, and optimally linked to other data sources. The expertise and scalable solutions needed to transform data to conform to network requirements are beyond the scope of many health care organizations and there is a need for practical tools that lower the barriers of data contribution to clinical research networks.
methodsWe designed and implemented a health data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code, while retaining manual aspects of the process that requires knowledge of complex coding syntax. This approach provides the flexibility required for the ETL of heterogeneous data, variations in semantic expertise, and transparency of transformation logic that are essential to implement ETL conventions across clinical research sharing networks. Processing workflows are directed by the ETL specifications guideline, developed by ETL designers with extensive knowledge of the structure and semantics of health data (i.e., "health data domain experts") and target common data model.
resultsD-ETL was implemented to perform ETL operations that load data from various sources with different database schema structures into the Observational Medical Outcome Partnership (OMOP) common data model. The results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health data transformation via automatic query generation to harmonize source datasets.
conclusionsD-ETL supports a flexible and transparent process to transform and load health data into a target data model. This approach offers a solution that lowers technical barriers that prevent data partners from participating in research data networks, and therefore, promotes the advancement of comparative effectiveness research using secondary electronic health data.
Indexed as
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What Socratic 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 Socratic graph.