ArticleEGEMS (Washington, DC)2017
A Framework for Classification of Electronic Health Data Extraction-Transformation-Loading Challenges in Data Network Participation.
Article in EGEMS (Washington, DC), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review.JMIR mHealth and uHealth · 2021Pooled it
- Article
- The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to RemicadeBMC medical research methodology · 2024Article
- A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research.medRxiv : the preprint server for health sciences · 2024Article
- MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.JAMIA open · 2024Article
- European Health Data & Evidence Network-learnings from building out a standardized international health data network.Journal of the American Medical Informatics Association : JAMIA · 2023Observational
- MENDS-on-FHIR: Leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.medRxiv : the preprint server for health sciences · 2023Article
- "In conferences, everyone goes 'health data is the future' ": an interview study on challenges in re-using EHR data for research in Clinical Data Warehouses.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Review of Clinical Research Informatics.Yearbook of medical informatics · 2020Review
- Data Science for Child Health.The Journal of pediatrics · 2019Review
- Merging heterogeneous clinical data to enable knowledge discovery.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Developing a Regional Distributed Data Network for Surveillance of Chronic Health Conditions: The Colorado Health Observation Regional Data Service.Journal of public health management and practice : JPHMPArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundContributing health data to national, regional, and local networks or registries requires data stored in local systems with local structures and codes to be extracted, transformed, and loaded into a standard format called a Common Data Model (CDM). These processes called Extract, Transform, Load (ETL) require data partners or contributors to invest in costly technical resources with specialized skills in data models, terminologies, and programming. Given the wide range of tasks, skills, and technologies required to transform data into a CDM, a classification of ETL challenges can help identify needed resources, which in turn may encourage data partners with less-technical capabilities to participate in data-sharing networks.
methodsWe conducted key-informant interviews with data partner representatives to survey the ETL challenges faced in clinical data research networks (CDRNs) and registries. A list of ETL challenges, organized into six themes was vetted during a one-day workshop with a wide range of network stakeholders including data partners, researchers, and policy experts.
resultsWe identified 24 technical ETL challenges related to the data sharing process. All of these ETL challenges were rated as "important" or "very important" by workshop participants using a five point Likert scale. Based on these findings, a framework for categorizing ETL challenges according to ETL phases, themes, and levels of data network participation was developed.
conclusionsOvercoming ETL technical challenges require significant investments in a broad array of information technologies and human resources. Identifying these technical obstacles can inform optimal resource allocation to minimize the barriers and cost of entry for new data partners into extant networks, which in turn can expand data networks' inclusiveness and diversity. This paper offers pertinent information and guiding framework that are relevant for data partners in ascertaining challenges associated with contributing data in data networks.
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.