Development and validation of an open-source pipeline for automatic population of case report forms from electronic health records: a pediatric multi-center prospective studyResearch in context
Alba Gutiérrez-Sacristán,
Simran Makwana,
Audrey Dionne,
Simran Mahanta,
Karla J. Dyer,
Faridis Serrano,
Carmen Watrin,
Pierre Pages,
Sajad Mousavi,
Anil Degala,
Jessica Lyons,
Danielle Pillion,
Joany M. Zachariasse,
Lara S. Shekerdemian,
Dongngan T. Truong,
Jane W. Newburger,
Paul Avillach
Affiliations
Alba Gutiérrez-Sacristán
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
Simran Makwana
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
Audrey Dionne
Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
Simran Mahanta
Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
Karla J. Dyer
Division of Cardiology, Department of Pediatrics, Texas Children's Hospital, Baylor College of Medicine, 6651 Main Street Legacy Tower MC E1920, Houston, TX, 77030, USA
Faridis Serrano
Division of Critical Care Medicine, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, 6621 Main Street, MC E1420, Houston, TX, 77030, USA
Carmen Watrin
Division of Congenital Heart Surgery, Department of Pediatrics, Texas Children's Hospital, 8718 Linkfair Lane, 77025, Houston, TX, USA
Pierre Pages
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
Sajad Mousavi
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
Anil Degala
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA; Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA
Jessica Lyons
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
Danielle Pillion
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
Joany M. Zachariasse
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
Lara S. Shekerdemian
Division of Critical Care Medicine, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, 6621 Main Street, MC E1420, Houston, TX, 77030, USA
Dongngan T. Truong
Division of Cardiology, Department of Pediatrics, University of Utah and Primary Children's Hospital, 81 North Mario Capecchi Drive, Salt Lake City, UT, USA
Jane W. Newburger
Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
Paul Avillach
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA; Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA; Corresponding author. Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.
Summary: Background: Clinical trials and registry studies are essential for advancing research and developing novel treatments. However, these studies rely on manual entry of thousands of variables for each patient. Repurposing real-world data can significantly simplify the data collection, reduce transcription errors, and make the data entry process more efficient, consistent, and cost-effective. Methods: We developed an open-source computational pipeline to collect laboratory and medication information from the electronic health record (EHR) data and populate case report forms. The pipeline was developed and validated with data from two independent pediatric hospitals in the US as part of the Long-terM OUtcomes after Multisystem Inflammatory Syndrome In Children (MUSIC) study. Our pipeline allowed the completion of two of the most time-consuming forms. We compared automatically extracted results with manually entered values in one hospital and applied the pipeline to a second hospital, where the output served as the primary data source for case report forms. Findings: We extracted and populated 51,845 laboratory and 4913 medication values for 159 patients in two hospitals participating in a prospective pediatric study. We evaluated pipeline performance against data for 104 patients manually entered by clinicians in one of the hospitals. The highest concordance was found during patient hospitalization, with 91.59% of the automatically extracted laboratory and medication values corresponding with the manually entered values. In addition to the successfully populated values, we identified an additional 13,396 laboratory and 567 medication values of interest for the study. Interpretation: The automatic data entry of laboratory and medication values during admission is feasible and has a high concordance with the manually entered data. By implementing this proof of concept, we demonstrate the quality of automatic data extraction and highlight the potential of secondary use of EHR data to advance medical science by improving data entry efficiency and expediting clinical research. Funding: NIH Grant 1OT3HL147154-01, U24HL135691, UG1HL135685.
