Bringing Code to Data: Do Not Forget Governance.
Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and dis...
| Publicado en: | Journal of Medical Internet Research Vol. 22; no. 7 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
JMIR Publications Inc.
Jul2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144902985&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144902985 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14394456 DNC jtl: Journal of Medical Internet Research issn: 14394456 maglogo: N pubinfo: dt: Jul2020 vid: 22 iid: 7 pid: 21567 pub: JMIR Publications Inc. place: Toronto, Ontario artinfo: ui: 144902985 144902985 NLM32540846 144902985 10.2196/18087 NLM32540846 144902985 ppct: 1 formats: tig: atl: Bringing Code to Data: Do Not Forget Governance. aug: au: Suver, Christine Thorogood, Adrian Doerr, Megan Wilbanks, John Knoppers, Bartha affil: Sage Bionetworks, Seattle, WA, United States sug: subj: Research, Medical Methods Communication Methods Human Reproducibility of Results Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales ab: Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing. For example, researchers can access data in high performance, secure, and auditable cloud computing environments without the need for copying or downloading. An alternative path to accessing data sets requiring additional protection is the model-to-data approach. In model-to-data, researchers submit algorithms to run on secure data sets that remain hidden. Model-to-data is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. Model-to-data has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that model-to-data can make a valuable addition to our data sharing arsenal, with 2 caveats. First, model-to-data should only be adopted where necessary to supplement rather than replace existing data-sharing approaches given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, although model-to-data reduces concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt model-to-data approaches without guidance on how to do so responsibly. To address this gap, we explored how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in a model-to-data context. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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