Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper.

Autor: Jones KH; Population Data Science, Medical School, Swansea University, Swansea, United Kingdom., Ford EM; Brighton and Sussex Medical School, Brighton, United Kingdom., Lea N; Institute of Health Informatics, University College London, London, United Kingdom., Griffiths LJ; Population Data Science, Medical School, Swansea University, Swansea, United Kingdom., Hassan L; Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom., Heys S; Population Data Science, Medical School, Swansea University, Swansea, United Kingdom., Squires E; Population Data Science, Medical School, Swansea University, Swansea, United Kingdom., Nenadic G; Department of Computer Science, University of Manchester & The Alan Turing Institute, Manchester, United Kingdom.
Jazyk: angličtina
Zdroj: Journal of medical Internet research [J Med Internet Res] 2020 Jun 29; Vol. 22 (6), pp. e16760. Date of Electronic Publication: 2020 Jun 29.
DOI: 10.2196/16760
Abstrakt: Background: Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product.
Objective: This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit.
Methods: We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text.
Results: We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders.
Conclusions: By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.
(©Kerina H Jones, Elizabeth M Ford, Nathan Lea, Lucy J Griffiths, Lamiece Hassan, Sharon Heys, Emma Squires, Goran Nenadic. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.06.2020.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje