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A two-site survey of medical center personnel’s willingness to share clinical data for research: implications for reproducible health NLP research

Authors :
Chunhua Weng
Carol Friedman
Casey A. Rommel
John F. Hurdle
Source :
BMC Medical Informatics and Decision Making, Vol 19, Iss S3, Pp 5-12 (2019)
Publication Year :
2019
Publisher :
BMC, 2019.

Abstract

Abstract Background A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes, that has breadth (from multiple institutions) as well as depth (as much individual data as possible). Methods We aimed to assess the degree to which individuals would be willing to contribute their health data to such a repository. A compact e-survey probed willingness to share demographic and clinical data categories. Participants were faculty, staff, and students in two geographically diverse major medical centers (Utah and New York). Such a sample could be expected to respond like a typical potential participant from the general public who is given complete and fully informed consent about the pros and cons of participating in a research study. Results Two thousand one hundred forty respondents completed the surveys. 56% of respondents were “somewhat/definitely willing” to share clinical data with identifiers, while 89% of respondents were “somewhat (17%)/definitely willing (72%)” to share without identifiers. Results were consistent across gender, age, and education, but there were some differences by geographical region. Individuals were most reluctant (50–74%) sharing mental health, substance abuse, and domestic violence data. Conclusions We conclude that a substantial fraction of potential patient participants, once educated about risks and benefits, would be willing to donate de-identified clinical data to a shared research repository. A slight majority even would be willing to share absent de-identification, suggesting that perceptions about data misuse are not a major concern. Such a repository of clinical notes should be invaluable for clinical NLP research and advancement.

Details

Language :
English
ISSN :
14726947
Volume :
19
Issue :
S3
Database :
Directory of Open Access Journals
Journal :
BMC Medical Informatics and Decision Making
Publication Type :
Academic Journal
Accession number :
edsdoj.bd80174326bb44d28c8545e139e394aa
Document Type :
article
Full Text :
https://doi.org/10.1186/s12911-019-0778-z