ArticleJournal of medical ethics2020
Should free-text data in electronic medical records be shared for research? A citizens' jury study in the UK.
Article in Journal of medical ethics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 4 of them syntheses that pooled it.
What it found
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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.
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.
Who cites it
36 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review.Journal of medical Internet research · 2024Pooled it
- The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature.Frontiers in digital health · 2021Pooled it
- Toward an Ethical Framework for the Text Mining of Social Media for Health Research: A Systematic Review.Frontiers in digital health · 2020Pooled it
- "Giving something back": A systematic review and ethical enquiry into public views on the use of patient data for research in the United Kingdom and the Republic of Ireland.Wellcome open research · 2018Pooled it
- Identification of patients with epilepsy using automated electronic health records phenotyping.Epilepsia · 2023Trial
- Trial
- Exploring patient motivations and preferences for medical data sharing with researchers: a simulation study using the iAgree platform.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- The Ethics of Leveraging Routinely Collected Patient Data for AI Development: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Quantifying the effects of pseudonymisation on epidemiological research reliability: a tailored evaluation using a clinical data warehouse.BMC medical informatics and decision making · 2026Article
- A software pipeline for medical information extraction with large language models, open source and suitable for oncology.NPJ precision oncology · 2025Review
- RADEX: a rule-based clinical and radiology data extraction tool demonstrated on thyroid ultrasound reports.European radiology · 2025Article
- A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data.NPJ digital medicine · 2025Article
- Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.Drug safety · 2025Article
- The European Health Data Space: an opportunity to strengthen citizen rights and engage citizens in health data governance.Frontiers in medicine · 2025Article
- Article
- What is the patient re-identification risk from using de-identified clinical free text data for health research?AI and ethics · 2025Review
- Data, dialogue, and design: patient and public involvement and engagement for natural language processing with real-world cancer data.Frontiers in digital health · 2025Article
- Applications of ChatGPT in Heart Failure Prevention, Diagnosis, Management, and Research: A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models.medRxiv : the preprint server for health sciences · 2024Article
- Based on Medicine, The Now and Future of Large Language Models.Cellular and molecular bioengineering · 2024Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUse of routinely collected patient data for research and service planning is an explicit policy of the UK National Health Service and UK government. Much clinical information is recorded in free-text letters, reports and notes. These text data are generally lost to research, due to the increased privacy risk compared with structured data. We conducted a citizens' jury which asked members of the public whether their medical free-text data should be shared for research for public benefit, to inform an ethical policy.
methodsEighteen citizens took part over 3 days. Jurors heard a range of expert presentations as well as arguments for and against sharing free text, and then questioned presenters and deliberated together. They answered a questionnaire on whether and how free text should be shared for research, gave reasons for and against sharing and suggestions for alleviating their concerns.
resultsJurors were in favour of sharing medical data and agreed this would benefit health research, but were more cautious about sharing free-text than structured data. They preferred processing of free text where a computer extracted information at scale. Their concerns were lack of transparency in uses of data, and privacy risks. They suggested keeping patients informed about uses of their data, and giving clear pathways to opt out of data sharing.
conclusionsInformed citizens suggested a transparent culture of research for the public benefit, and continuous improvement of technology to protect patient privacy, to mitigate their concerns regarding privacy risks of using patient text data.
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What OpenQuestion holds
Registered trials
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.