Evidence map›Paper›PMID 32597785›Full record

ArticleJournal of medical Internet research2020

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

Kerina H Jones, Elizabeth M Ford, Nathan Lea, Lucy J Griffiths, Lamiece Hassan, Sharon Heys, Emma Squires, Goran Nenadic

Abstract readConsensus Statement
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Evaluation of trajectory analysis for disease risk assessment: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026
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  3. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Kerina H JonesPopulation Data Science, Medical School, Swansea University, Swansea, United Kingdom.ORCID 0000-0001-8164-3718
Elizabeth M FordBrighton and Sussex Medical School, Brighton, United Kingdom.ORCID 0000-0001-5613-8509
Nathan LeaInstitute of Health Informatics, University College London, London, United Kingdom.ORCID 0000-0001-5056-0602
Lucy J GriffithsPopulation Data Science, Medical School, Swansea University, Swansea, United Kingdom.ORCID 0000-0001-9230-624X
Lamiece HassanDivision of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom.ORCID 0000-0002-5888-422X
Sharon HeysPopulation Data Science, Medical School, Swansea University, Swansea, United Kingdom.ORCID 0000-0002-3007-644X
Emma SquiresPopulation Data Science, Medical School, Swansea University, Swansea, United Kingdom.ORCID 0000-0003-0693-5846
Goran NenadicDepartment of Computer Science, University of Manchester & The Alan Turing Institute, Manchester, United Kingdom.ORCID 0000-0003-0795-5363

Funding

Medical Research Council MC_PC_13042Medical Research Council MR/K006665/1Medical Research Council MR/S004025/1
6 · The paper itself

Abstract

backgroundClinical 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.

objectiveThis 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.

methodsWe 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.

resultsWe 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.

conclusionsBy 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.

Indexed as

Data AnalysisHumansReference StandardsText Messagingethicalfree-text datainformation governancelegalpublic engagementsocial implications

Identifiers

PMID32597785
PMCPMC7367542

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.