Evidence map›Paper›PMID 38027579›Full record

ArticleHeliyon2023

Development and validation of the SickKids Enterprise-wide Data in Azure Repository (SEDAR).

Lin Lawrence Guo, Maryann Calligan, Emily Vettese, Sadie Cook, George Gagnidze, Oscar Han, Jiro Inoue, Joshua Lemmon, Johnson Li, Medhat Roshdi and 3 more

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Observational
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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

13 authors.

Lin Lawrence GuoProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
Maryann CalliganProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
Emily VetteseProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
Sadie CookProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
George GagnidzeInformation Management Technology, The Hospital for Sick Children, Toronto, Canada.
Oscar HanInformation Management Technology, The Hospital for Sick Children, Toronto, Canada.
Jiro InoueProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
Joshua LemmonProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
Johnson LiInformation Management Technology, The Hospital for Sick Children, Toronto, Canada.
Medhat RoshdiInformation Management Technology, The Hospital for Sick Children, Toronto, Canada.
Bohdan SadovyInformation Management Technology, The Hospital for Sick Children, Toronto, Canada.
Steven WallaceInformation Management Technology, The Hospital for Sick Children, Toronto, Canada.
Lillian SungProgram in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To describe the processes developed by The Hospital for Sick Children (SickKids) to enable utilization of electronic health record (EHR) data by creating sequentially transformed schemas for use across multiple user types. Methods: We used Microsoft Azure as the cloud service provider and named this effort the SickKids Enterprise-wide Data in Azure Repository (SEDAR). Epic Clarity data from on-premises was copied to a virtual network in Microsoft Azure. Three sequential schemas were developed. The Filtered Schema added a filter to retain only SickKids and valid patients. The Curated Schema created a data structure that was easier to navigate and query. Each table contained a logical unit such as patients, hospital encounters or laboratory tests. Data validation of randomly sampled observations in the Curated Schema was performed. The SK-OMOP Schema was designed to facilitate research and machine learning. Two individuals mapped medical elements to standard Observational Medical Outcomes Partnership (OMOP) concepts. Results: A copy of Clarity data was transferred to Microsoft Azure and updated each night using log shipping. The Filtered Schema and Curated Schema were implemented as stored procedures and executed each night with incremental updates or full loads. Data validation required up to 16 iterations for each Curated Schema table. OMOP concept mapping achieved at least 80 % coverage for each SK-OMOP table. Conclusions: We described our experience in creating three sequential schemas to address different EHR data access requirements. Future work should consider replicating this approach at other institutions to determine whether approaches are generalizable.

Indexed as

Electronic health recordsMicrosoft AzureOMOP-CDMSchemaValidation

Identifiers

PMID38027579
PMCPMC10661187

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.