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,
Bohdan Sadovy,
Steven Wallace,
Lillian Sung
Affiliations
Lin Lawrence Guo
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
Maryann Calligan
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
Emily Vettese
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
Sadie Cook
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
George Gagnidze
Information Management Technology, The Hospital for Sick Children, Toronto, Canada
Oscar Han
Information Management Technology, The Hospital for Sick Children, Toronto, Canada
Jiro Inoue
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
Joshua Lemmon
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
Johnson Li
Information Management Technology, The Hospital for Sick Children, Toronto, Canada
Medhat Roshdi
Information Management Technology, The Hospital for Sick Children, Toronto, Canada
Bohdan Sadovy
Information Management Technology, The Hospital for Sick Children, Toronto, Canada
Steven Wallace
Information Management Technology, The Hospital for Sick Children, Toronto, Canada
Lillian Sung
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada; Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Canada; Corresponding author. The Division of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G1X8, Canada.
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.
