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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

      research-article
      1 , 1 , 2 , 2 ,
      BMC Medical Informatics and Decision Making
      BioMed Central
      The Sixth IEEE International Conference on Healthcare Informatics (ICHI 2018)
      4-7 June 2018

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          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.

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          Most cited references14

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          Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1.

          The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured four tracks. The first of these was the de-identification track focused on identifying protected health information (PHI) in longitudinal clinical narratives. The longitudinal nature of clinical narratives calls particular attention to details of information that, while benign on their own in separate records, can lead to identification of patients in combination in longitudinal records. Accordingly, the 2014 de-identification track addressed a broader set of entities and PHI than covered by the Health Insurance Portability and Accountability Act - the focus of the de-identification shared task that was organized in 2006. Ten teams tackled the 2014 de-identification task and submitted 22 system outputs for evaluation. Each team was evaluated on their best performing system output. Three of the 10 systems achieved F1 scores over .90, and seven of the top 10 scored over .75. The most successful systems combined conditional random fields and hand-written rules. Our findings indicate that automated systems can be very effective for this task, but that de-identification is not yet a solved problem.
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            Ushering in a new era of open science through data sharing: the wall must come down.

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              Willingness to share personal health record data for care improvement and public health: a survey of experienced personal health record users

              Background Data stored in personally controlled health records (PCHRs) may hold value for clinicians and public health entities, if patients and their families will share them. We sought to characterize consumer willingness and unwillingness (reticence) to share PCHR data across health topics, and with different stakeholders, to advance understanding of this issue. Methods Cross-sectional 2009 Web survey of repeat PCHR users who were patients over 18 years old or parents of patients, to assess willingness to share their PCHR data with an-out-of-hospital provider to support care, and the state/local public health authority to support monitoring; the odds of reticence to share PCHR information about ten exemplary health topics were estimated using a repeated measures approach. Results Of 261 respondents (56% response rate), more reported they would share all information with the state/local public health authority (63.3%) than with an out-of-hospital provider (54.1%) (OR 1.5, 95% CI 1.1, 1.9; p = .005); few would not share any information with these parties (respectively, 7.9% and 5.2%). For public health sharing, reticence was higher for most topics compared to contagious illness (ORs 4.9 to 1.4, all p-values < .05), and reflected concern about anonymity (47.2%), government insensitivity (41.5%), discrimination (24%). For provider sharing, reticence was higher for all topics compared to contagious illness (ORs 6.3 to 1.5, all p-values < .05), and reflected concern for relevance (52%), disclosure to insurance (47.6%) and/or family (20.5%). Conclusions Pediatric patients and their families are often willing to share electronic health information to support health improvement, but remain cautious. Robust trust models for PCHR sharing are needed.
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                Author and article information

                Contributors
                chunhua@columbia.edu
                cf9@cumc.columbia.edu
                casey.rommel@hsc.utah.edu
                john.hurdle@utah.edu
                Conference
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                4 April 2019
                4 April 2019
                2019
                : 19
                Issue : Suppl 3 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. VV was co-author of two papers in the supplement, the peer review of these papers were managed by HX and YZ. HX and YZ were co-authors of a paper in the supplement, the peer review was managed by YW. YW was co-author of two papers in the supplement, the peer review of these papers were managed by VV. No other competing interests were declared.
                : 70
                Affiliations
                [1 ]ISNI 0000000419368729, GRID grid.21729.3f, Department of Biomedical Informatics, , Columbia University, ; New York City, NY 10025 USA
                [2 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Biomedical Informatics, , University of Utah, ; Salt Lake City, UT 84108 USA
                Article
                778
                10.1186/s12911-019-0778-z
                6448185
                30943963
                ee1f87fc-198e-4b02-bbf7-f84395ed433b
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                The Sixth IEEE International Conference on Healthcare Informatics (ICHI 2018)
                New York, NY, USA
                4-7 June 2018
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                © The Author(s) 2019

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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