The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team‐based approach to clinically validated, research‐ready electronic health record data

Abstract Introduction The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, val...

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Main Authors: Andrea L. C. Schneider, Jennifer C. Ginestra, Meeta Prasad Kerlin, Michael G. S. Shashaty, Todd A. Miano, Daniel S. Herman, Oscar J. L. Mitchell, Rachel Bennett, Alexander T. Moffett, John Chandler, Atul Kalanuria, Zahra Faraji, Nicholas S. Bishop, Benjamin Schmid, Angela T. Chen, Kathryn H. Bowles, Thomas Joseph, Rachel Kohn, Rachel R. Kelz, George L. Anesi, Monisha Kumar, Ari B. Friedman, Emily Vail, Nuala J. Meyer, Blanca E. Himes, Gary E. Weissman
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Learning Health Systems
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Online Access:https://doi.org/10.1002/lrh2.10439
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author Andrea L. C. Schneider
Jennifer C. Ginestra
Meeta Prasad Kerlin
Michael G. S. Shashaty
Todd A. Miano
Daniel S. Herman
Oscar J. L. Mitchell
Rachel Bennett
Alexander T. Moffett
John Chandler
Atul Kalanuria
Zahra Faraji
Nicholas S. Bishop
Benjamin Schmid
Angela T. Chen
Kathryn H. Bowles
Thomas Joseph
Rachel Kohn
Rachel R. Kelz
George L. Anesi
Monisha Kumar
Ari B. Friedman
Emily Vail
Nuala J. Meyer
Blanca E. Himes
Gary E. Weissman
author_facet Andrea L. C. Schneider
Jennifer C. Ginestra
Meeta Prasad Kerlin
Michael G. S. Shashaty
Todd A. Miano
Daniel S. Herman
Oscar J. L. Mitchell
Rachel Bennett
Alexander T. Moffett
John Chandler
Atul Kalanuria
Zahra Faraji
Nicholas S. Bishop
Benjamin Schmid
Angela T. Chen
Kathryn H. Bowles
Thomas Joseph
Rachel Kohn
Rachel R. Kelz
George L. Anesi
Monisha Kumar
Ari B. Friedman
Emily Vail
Nuala J. Meyer
Blanca E. Himes
Gary E. Weissman
author_sort Andrea L. C. Schneider
collection DOAJ
description Abstract Introduction The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, validated, and ready for use by stakeholders. We describe an in‐progress effort to overcome these challenges with cooperative, systematic data extraction and validation. Methods A multi‐disciplinary team of investigators collaborated to create the Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) Project dataset, which captures EHR data from six hospitals within the University of Pennsylvania Health System. Analysts and clinical researchers jointly iteratively reviewed SQL queries and their output to validate desired data elements. Data from patients aged ≥18 years with at least one encounter at an acute care hospital or hospice occurring since 7/1/2017 were included. The CIRCE Project includes three layers: (1) raw data comprised of direct SQL query output, (2) cleaned data with errors removed, and (3) transformed data with standardized implementations of commonly used case definitions and clinical scores. Results Between July 1, 2017 and December 31, 2023, the dataset captured 1 629 920 encounters from 740 035 patients. Most encounters were emergency department only visits (n = 965 834, 59.3%), followed by inpatient admissions without an intensive care unit admission (n = 518 367, 23.7%). The median age was 46.9 years (25th–75th percentiles = 31.1–64.7) at the time of the first encounter. Most patients were female (n = 418 303, 56.5%), a significant proportion were of non‐White race (n = 272 018, 36.8%), and 54 625 (7.4%) were of Hispanic/Latino ethnicity. Conclusions The CIRCE Project represents a novel cooperative research model to capture clinically validated EHR data from a large diverse academic health system in the greater Philadelphia region and is designed to facilitate collaboration and data sharing to support learning health system activities. Ultimately, these data will be de‐identified and converted to a publicly available resource.
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spelling doaj-art-cc3fd6f94d73492fa7782449fd7f5f0f2025-01-15T08:51:32ZengWileyLearning Health Systems2379-61462025-01-0191n/an/a10.1002/lrh2.10439The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team‐based approach to clinically validated, research‐ready electronic health record dataAndrea L. C. Schneider0Jennifer C. Ginestra1Meeta Prasad Kerlin2Michael G. S. Shashaty3Todd A. Miano4Daniel S. Herman5Oscar J. L. Mitchell6Rachel Bennett7Alexander T. Moffett8John Chandler9Atul Kalanuria10Zahra Faraji11Nicholas S. Bishop12Benjamin Schmid13Angela T. Chen14Kathryn H. Bowles15Thomas Joseph16Rachel Kohn17Rachel R. Kelz18George L. Anesi19Monisha Kumar20Ari B. Friedman21Emily Vail22Nuala J. Meyer23Blanca E. Himes24Gary E. Weissman25Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Pathology and Laboratory Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USAPalliative and Advanced Illness Research (PAIR) Center University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USAUniversity of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USALeonard Davis Institute of Health Economics University of Pennsylvania Philadelphia Pennsylvania USADepartment of Anesthesia and Critical Care University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USALeonard Davis Institute of Health Economics University of Pennsylvania Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USALeonard Davis Institute of Health Economics University of Pennsylvania Philadelphia Pennsylvania USALeonard Davis Institute of Health Economics University of Pennsylvania Philadelphia Pennsylvania USADepartment of Medicine University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USADepartment of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USAAbstract Introduction The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, validated, and ready for use by stakeholders. We describe an in‐progress effort to overcome these challenges with cooperative, systematic data extraction and validation. Methods A multi‐disciplinary team of investigators collaborated to create the Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) Project dataset, which captures EHR data from six hospitals within the University of Pennsylvania Health System. Analysts and clinical researchers jointly iteratively reviewed SQL queries and their output to validate desired data elements. Data from patients aged ≥18 years with at least one encounter at an acute care hospital or hospice occurring since 7/1/2017 were included. The CIRCE Project includes three layers: (1) raw data comprised of direct SQL query output, (2) cleaned data with errors removed, and (3) transformed data with standardized implementations of commonly used case definitions and clinical scores. Results Between July 1, 2017 and December 31, 2023, the dataset captured 1 629 920 encounters from 740 035 patients. Most encounters were emergency department only visits (n = 965 834, 59.3%), followed by inpatient admissions without an intensive care unit admission (n = 518 367, 23.7%). The median age was 46.9 years (25th–75th percentiles = 31.1–64.7) at the time of the first encounter. Most patients were female (n = 418 303, 56.5%), a significant proportion were of non‐White race (n = 272 018, 36.8%), and 54 625 (7.4%) were of Hispanic/Latino ethnicity. Conclusions The CIRCE Project represents a novel cooperative research model to capture clinically validated EHR data from a large diverse academic health system in the greater Philadelphia region and is designed to facilitate collaboration and data sharing to support learning health system activities. Ultimately, these data will be de‐identified and converted to a publicly available resource.https://doi.org/10.1002/lrh2.10439electronic health record (EHR)learning health systemslearning healthcare systems
spellingShingle Andrea L. C. Schneider
Jennifer C. Ginestra
Meeta Prasad Kerlin
Michael G. S. Shashaty
Todd A. Miano
Daniel S. Herman
Oscar J. L. Mitchell
Rachel Bennett
Alexander T. Moffett
John Chandler
Atul Kalanuria
Zahra Faraji
Nicholas S. Bishop
Benjamin Schmid
Angela T. Chen
Kathryn H. Bowles
Thomas Joseph
Rachel Kohn
Rachel R. Kelz
George L. Anesi
Monisha Kumar
Ari B. Friedman
Emily Vail
Nuala J. Meyer
Blanca E. Himes
Gary E. Weissman
The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team‐based approach to clinically validated, research‐ready electronic health record data
Learning Health Systems
electronic health record (EHR)
learning health systems
learning healthcare systems
title The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team‐based approach to clinically validated, research‐ready electronic health record data
title_full The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team‐based approach to clinically validated, research‐ready electronic health record data
title_fullStr The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team‐based approach to clinically validated, research‐ready electronic health record data
title_full_unstemmed The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team‐based approach to clinically validated, research‐ready electronic health record data
title_short The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team‐based approach to clinically validated, research‐ready electronic health record data
title_sort complete inpatient record using comprehensive electronic data circe project a team based approach to clinically validated research ready electronic health record data
topic electronic health record (EHR)
learning health systems
learning healthcare systems
url https://doi.org/10.1002/lrh2.10439
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