Evidence map›Paper›PMID 36220109›Full record

ArticleMethods of information in medicine2022

TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse.

Miguel Pedrera-Jiménez, Noelia García-Barrio, Paula Rubio-Mayo, Alberto Tato-Gómez, Juan Luis Cruz-Bermúdez, José Luis Bernal-Sobrino, Adolfo Muñoz-Carrero, Pablo Serrano-Balazote

Abstract read
In one paragraph

Article in Methods of information in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Miguel Pedrera-JiménezData Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain.
Noelia García-BarrioData Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain.
Paula Rubio-MayoData Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain.
Alberto Tato-GómezData Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain.
Juan Luis Cruz-BermúdezData Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain.
José Luis Bernal-SobrinoData Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain.
Adolfo Muñoz-CarreroDigital Health Research Unit, Instituto de Salud Carlos III, Madrid, Spain.
Pablo Serrano-BalazoteData Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain.

Funding

PI18/00981 Ministerio de Economía y CompetitividadPI18/01047 Instituto de Salud Carlos IIIPI18CIII/00019 Instituto de Salud Carlos III
6 · The paper itself

Abstract

backgroundDuring the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based on transparent, homogeneous, and formal methodologies, making them understandable, reproducible, and auditable.

objectivesThis study aims to design and implement a methodology, according with FAIR Principles, for building ETL processes (focused on data extraction, selection, and transformation) for EHR reuse in a transparent and flexible manner, applicable to any clinical condition and health care organization.

methodsThe proposed methodology comprises four stages: (1) analysis of secondary use models and identification of data operations, based on internationally used clinical repositories, case report forms, and aggregated datasets; (2) modeling and formalization of data operations, through the paradigm of the Detailed Clinical Models; (3) agnostic development of data operations, selecting SQL and R as programming languages; and (4) automation of the ETL instantiation, building a formal configuration file with XML.

resultsFirst, four international projects were analyzed to identify 17 operations, necessary to obtain datasets according to the specifications of these projects from the EHR. With this, each of the data operations was formalized, using the ISO 13606 reference model, specifying the valid data types as arguments, inputs and outputs, and their cardinality. Then, an agnostic catalog of data was developed through data-oriented programming languages previously selected. Finally, an automated ETL instantiation process was built from an ETL configuration file formally defined.

conclusionsThis study has provided a transparent and flexible solution to the difficulty of making the processes for obtaining EHR-derived data for secondary use understandable, auditable, and reproducible. Moreover, the abstraction carried out in this study means that any previous EHR reuse methodology can incorporate these results into them.

Indexed as

COVID-19Electronic Health RecordsHumansPandemics

Identifiers

PMID36220109
PMCPMC9788916

What Socratic holds

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Registered trials

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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.