Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform.
data abstraction
health records
machine learning
multiple sclerosis
real-world data
Journal
JAMIA open
ISSN: 2574-2531
Titre abrégé: JAMIA Open
Pays: United States
ID NLM: 101730643
Informations de publication
Date de publication:
Apr 2022
Apr 2022
Historique:
received:
22
03
2021
revised:
21
10
2021
accepted:
13
12
2021
entrez:
14
2
2022
pubmed:
15
2
2022
medline:
15
2
2022
Statut:
epublish
Résumé
The FlywheelMS study will explore the use of a real-world health record data set generated by PicnicHealth, a patient-centric health records platform, to improve understanding of disease course and patterns of care for patients with multiple sclerosis (MS). The FlywheelMS study aims to enroll 5000 adults with MS in the United States to create a large, deidentified, longitudinal data set for clinical research. PicnicHealth obtains health records, including paper charts, electronic health records, and radiology imaging files from any healthcare site. Using a large-scale health record processing pipeline, PicnicHealth abstracts standard and condition-specific data elements from structured (eg, laboratory test results) and unstructured (eg, narrative) text and maps these to standardized medical vocabularies. Researchers can use the resulting data set to answer empirical questions and study participants can access and share their harmonized health records using PicnicHealth's web application. As of November 24, 2020, more than 4176 participants from 49 of 50 US states have enrolled in the FlywheelMS study. A median of 200 pages of records have been collected from 14 different doctors over 8 years per participant. Abstraction precision, established through inter-abstractor agreement, is as high as 97.8% when identifying and mapping data elements to a standard ontology. Using a commercial health records platform, the FlywheelMS study is generating a real-world, multimodal data set that could provide valuable insights about patients with MS. This approach to data collection and abstraction is disease-agnostic and could be used to address other clinical research questions in the future.
Identifiants
pubmed: 35155999
doi: 10.1093/jamiaopen/ooab110
pii: ooab110
pmc: PMC8827034
doi:
Types de publication
Journal Article
Langues
eng
Pagination
ooab110Informations de copyright
© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.
Références
Future Oncol. 2016 May;12(10):1261-74
pubmed: 27096309
Radiology. 2016 Feb;278(2):563-77
pubmed: 26579733
Neuropsychiatr Dis Treat. 2017 May 18;13:1349-1357
pubmed: 28572730
Int J Med Inform. 2008 May;77(5):291-304
pubmed: 17951106
Clin Res Cardiol. 2017 Jan;106(1):1-9
pubmed: 27557678
J Community Health. 2014 Jun;39(3):562-71
pubmed: 24310703
Br J Radiol. 2017 Feb;90(1070):20160665
pubmed: 27936886
Clin Pharmacol Ther. 2015 Nov;98(5):514-21
pubmed: 26234275
Circulation. 2008 Sep 16;118(12):1294-303
pubmed: 18794402
US Statut Large. 1996 Aug 21;110:1936-2103
pubmed: 16477734
Acta Orthop. 2011 Feb;82(1):56-63
pubmed: 21189113
BMC Neurol. 2016 Aug 02;16:124
pubmed: 27484848
J Am Med Inform Assoc. 2010 Sep-Oct;17(5):524-7
pubmed: 20819856
Ann Fam Med. 2011 Jul-Aug;9(4):351-8
pubmed: 21747107
Perspect Health Inf Manag. 2016 Oct 01;13(Fall):1g
pubmed: 27843424
J Biomed Inform. 2017 Aug;72:60-66
pubmed: 28684255
J Am Med Inform Assoc. 2016 May;23(3):627-34
pubmed: 26661718
Cancer. 2013 Aug 15;119(16):2956-63
pubmed: 23674318
Value Health. 2017 Jul - Aug;20(7):858-865
pubmed: 28712614
Annu Rev Public Health. 2016;37:61-81
pubmed: 26667605