ArticleHeliyon2023
Development and validation of the SickKids Enterprise-wide Data in Azure Repository (SEDAR).
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.
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Who cites it
12 citing papers in PubMed.
- Multi-Center Evaluation of Opioid Administration or Prescription in Pediatric Oncology Patients Using the OMOP CDM.Cancer medicine · 2026Observational
- CMC-ID: identifying children and youth with medical complexity using an electronic health record.BMJ health & care informatics · 2026Article
- Implementation and Evaluation of an Alternative Electronic Health Record Tool for Ordering Blood Products in Pediatric Oncology and Stem Cell Transplantation: Mixed Methods Analysis.JMIR medical informatics · 2026Article
- Do biologics improve health outcomes in children with severe asthma? A cohort study.ERJ open research · 2026Article
- Identifying Hemophagocytic Lymphohistiocytosis and Describing Outcomes Using Computable Phenotypes: Retrospective Cohort Study.JMIR cancer · 2026Article
- Characterization and validation of electronic medical record data for pharmacoepidemiologic research.Frontiers in pharmacology · 2026Article
- Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.BMC cancer · 2025Article
- Development and prospective evaluation of a machine learning model to predict serious cardiac outcomes among paediatric cardiac inpatients.BMJ digital health & AI · 2025Article
- A roadmap to implementing machine learning in healthcare: from concept to practice.Frontiers in digital health · 2025Article
- Retrospective evaluation of a machine learning model to facilitate pharmacogenetic testing.BMJ digital health & AI · 2025Article
- A multi-center study on the adaptability of a shared foundation model for electronic health records.NPJ digital medicine · 2024Article
- Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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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 OpenQuestion graph.