Evidence map›Paper›PMID 37133927›Full record

ArticleJMIR medical informatics2023

Understanding Views Around the Creation of a Consented, Donated Databank of Clinical Free Text to Develop and Train Natural Language Processing Models for Research: Focus Group Interviews With Stakeholders.

Natalie K Fitzpatrick, Richard Dobson, Angus Roberts, Kerina Jones, Anoop D Shah, Goran Nenadic, Elizabeth Ford

Abstract read
In one paragraph

Article in JMIR medical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

7 authors.

Natalie K FitzpatrickInstitute of Health Informatics, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0001-5000-2520
Richard DobsonDepartment of Biostatistics and Health Informatics, King's College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-4224-9245
Angus RobertsDepartment of Biostatistics and Health Informatics, King's College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-4570-9801
Kerina JonesDepartment of Population Data Science, Swansea University Medical School, Swansea, United Kingdom.ORCID https://orcid.org/0000-0001-8164-3718
Anoop D ShahInstitute of Health Informatics, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-8907-5724
Goran NenadicDepartment of Computer Science, University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0003-0795-5363
Elizabeth FordBrighton and Sussex Medical School, Brighton, United Kingdom.ORCID https://orcid.org/0000-0001-5613-8509

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInformation stored within electronic health records is often recorded as unstructured text. Special computerized natural language processing (NLP) tools are needed to process this text; however, complex governance arrangements make such data in the National Health Service hard to access, and therefore, it is difficult to use for research in improving NLP methods. The creation of a donated databank of clinical free text could provide an important opportunity for researchers to develop NLP methods and tools and may circumvent delays in accessing the data needed to train the models. However, to date, there has been little or no engagement with stakeholders on the acceptability and design considerations of establishing a free-text databank for this purpose.

objectiveThis study aimed to ascertain stakeholder views around the creation of a consented, donated databank of clinical free text to help create, train, and evaluate NLP for clinical research and to inform the potential next steps for adopting a partner-led approach to establish a national, funded databank of free text for use by the research community.

methodsWeb-based in-depth focus group interviews were conducted with 4 stakeholder groups (patients and members of the public, clinicians, information governance leads and research ethics members, and NLP researchers).

resultsAll stakeholder groups were strongly in favor of the databank and saw great value in creating an environment where NLP tools can be tested and trained to improve their accuracy. Participants highlighted a range of complex issues for consideration as the databank is developed, including communicating the intended purpose, the approach to access and safeguarding the data, who should have access, and how to fund the databank. Participants recommended that a small-scale, gradual approach be adopted to start to gather donations and encouraged further engagement with stakeholders to develop a road map and set of standards for the databank.

conclusionsThese findings provide a clear mandate to begin developing the databank and a framework for stakeholder expectations, which we would aim to meet with the databank delivery.

Indexed as

consentdatabankelectronic health recordsfree textgovernancenatural language processingpublic involvementunstructured text

Identifiers

PMID37133927
PMCPMC10193205

What OpenQuestion holds

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LicenceCC BY
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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.