Development and validation of an open-source pipeline for automatic population of case report forms from electronic health records: a pediatric multi-center prospective study.

Data collection Electronic health records Multicenter study Prospective studies

Journal

EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039

Informations de publication

Date de publication:
16 Sep 2024
Historique:
received: 10 05 2024
revised: 22 08 2024
accepted: 30 08 2024
medline: 18 9 2024
pubmed: 18 9 2024
entrez: 17 9 2024
Statut: aheadofprint

Résumé

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. 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. 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. 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. NIH Grant 1OT3HL147154-01, U24HL135691, UG1HL135685.

Sections du résumé

BACKGROUND 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 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 RESULTS
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 CONCLUSIONS
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 BACKGROUND
NIH Grant 1OT3HL147154-01, U24HL135691, UG1HL135685.

Identifiants

DOI: 10.1016/j.ebiom.2024.105337 PMID: 39288532
pubmed: 39288532
pii: S2352-3964(24)00373-6
doi: 10.1016/j.ebiom.2024.105337
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105337

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests The authors declare no competing interest.

Auteurs

Alba Gutiérrez-Sacristán (A)

Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.

Simran Makwana (S)

Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.

Audrey Dionne (A)

Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA.

Simran Mahanta (S)

Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA.

Karla J Dyer (KJ)

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 (F)

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 (C)

Division of Congenital Heart Surgery, Department of Pediatrics, Texas Children's Hospital, 8718 Linkfair Lane, 77025, Houston, TX, USA.

Pierre Pages (P)

Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.

Sajad Mousavi (S)

Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.

Anil Degala (A)

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 (J)

Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.

Danielle Pillion (D)

Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.

Joany M Zachariasse (JM)

Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.

Lara S Shekerdemian (LS)

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 (DT)

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 (JW)

Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA.

Paul Avillach (P)

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. Electronic address: paul_avillach@hms.harvard.edu.

Classifications MeSH