ArticleBMJ open2023
Developing an ethical framework-guided instrument for assessing bias in EHR-based Big Data studies: a research protocol.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial Intelligence and Its Role in Predicting Periprosthetic Joint Infections.Biomedicines · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
introductionThe emergence of Big Data health research has exponentially advanced the fields of medicine and public health but has also faced many ethical challenges. One of most worrying but still under-researched aspects of the ethical issues is the risk of potential biases in data sets (eg, electronic health records (EHR) data) as well as in the data curation and acquisition cycles. This study aims to develop, refine and pilot test an ethical framework-guided instrument for assessing bias in Big Data research using EHR data sets. METHODS AND ANALYSIS: Ethical analysis and instrument development (ie, the EHR bias assessment guideline) will be implemented through an iterative process composed of literature/policy review, content analysis and interdisciplinary dialogues and discussion. The ethical framework and EHR bias assessment guideline will be iteratively refined and integrated with preliminary summaries of results in a way that informs subsequent research. We will engage data curators, end-user researchers, healthcare workers and patient representatives throughout all iterative cycles using various formats including in-depth interviews of key stakeholders, panel discussions and charrette workshops. The developed EHR bias assessment guideline will be pilot tested in an existing National Institutes of Health (NIH) funded Big Data HIV project (R01AI164947). ETHICS AND DISSEMINATION: The study was approved by Institutional Review Boards at the University of South Carolina (Pro00122501). Informed consent will be provided by the participants in the in-depth interviews. Study findings will be disseminated with key stakeholders, presented at relevant workshops and academic conferences, and published in peer-reviewed journals.
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Registered trials
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