Cargando…

Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform

OBJECTIVE: 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). MATERIALS AND METHODS: The Flyw...

Descripción completa

Detalles Bibliográficos
Autores principales: Hanson, Gillian, Chitnis, Tanuja, Williams, Mitzi J, Gan, Ryan William, Julian, Laura, Mace, Kieran, Chia, Jenny, Wormser, David, Martinec, Michael, Astorino, Troy, Leviner, Noga, Maung, Pye, Jan, Asif, Belendiuk, Katherine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827034/
https://www.ncbi.nlm.nih.gov/pubmed/35155999
http://dx.doi.org/10.1093/jamiaopen/ooab110
_version_ 1784647546856013824
author Hanson, Gillian
Chitnis, Tanuja
Williams, Mitzi J
Gan, Ryan William
Julian, Laura
Mace, Kieran
Chia, Jenny
Wormser, David
Martinec, Michael
Astorino, Troy
Leviner, Noga
Maung, Pye
Jan, Asif
Belendiuk, Katherine
author_facet Hanson, Gillian
Chitnis, Tanuja
Williams, Mitzi J
Gan, Ryan William
Julian, Laura
Mace, Kieran
Chia, Jenny
Wormser, David
Martinec, Michael
Astorino, Troy
Leviner, Noga
Maung, Pye
Jan, Asif
Belendiuk, Katherine
author_sort Hanson, Gillian
collection PubMed
description OBJECTIVE: 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). MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.
format Online
Article
Text
id pubmed-8827034
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-88270342022-02-10 Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform Hanson, Gillian Chitnis, Tanuja Williams, Mitzi J Gan, Ryan William Julian, Laura Mace, Kieran Chia, Jenny Wormser, David Martinec, Michael Astorino, Troy Leviner, Noga Maung, Pye Jan, Asif Belendiuk, Katherine JAMIA Open Research and Applications OBJECTIVE: 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). MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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. Oxford University Press 2022-01-17 /pmc/articles/PMC8827034/ /pubmed/35155999 http://dx.doi.org/10.1093/jamiaopen/ooab110 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Research and Applications
Hanson, Gillian
Chitnis, Tanuja
Williams, Mitzi J
Gan, Ryan William
Julian, Laura
Mace, Kieran
Chia, Jenny
Wormser, David
Martinec, Michael
Astorino, Troy
Leviner, Noga
Maung, Pye
Jan, Asif
Belendiuk, Katherine
Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform
title Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform
title_full Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform
title_fullStr Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform
title_full_unstemmed Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform
title_short Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform
title_sort generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827034/
https://www.ncbi.nlm.nih.gov/pubmed/35155999
http://dx.doi.org/10.1093/jamiaopen/ooab110
work_keys_str_mv AT hansongillian generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT chitnistanuja generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT williamsmitzij generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT ganryanwilliam generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT julianlaura generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT macekieran generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT chiajenny generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT wormserdavid generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT martinecmichael generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT astorinotroy generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT levinernoga generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT maungpye generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT janasif generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform
AT belendiukkatherine generatingrealworlddatafromhealthrecordsdesignofapatientcentricstudyinmultiplesclerosisusingacommercialhealthrecordsplatform