ReviewJournal of advanced nursing2026
Information Distortion in Electronic Health Records: A Concept Analysis.
Review in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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
3 citing papers in PubMed.
- Manifestations and Potential Consequences of Information Distortion in Electronic Nursing Records: Qualitative Study.JMIR nursing · 2026Article
- Information Distortion in Electronic Health Records: A Concept Analysis.Journal of advanced nursing · 2026Review
- Artificial intelligence in acute and critical care: current challenges and strategic solutions.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
aimsTo conceptualise information distortion in Electronic Health Records (EHRs), with the goal of providing a theoretical foundation for improving documentation practices.
designA concept analysis.
methodsWalker and Avant's strategy for concept analysis was used. The defining attributes, antecedents and consequences were identified. DATA SOURCES: A comprehensive search was conducted across PubMed, Web of Science, Embase, CINAHL and Scopus from their inception to December 2024. Studies published in English that addressed information distortion in EHRs were included.
resultsA total of 37 studies were included. The three defining attributes were: real-world health truth, representation of reality and mismatch relationship. Antecedents were divided into five categories: people-related factors, equipment factors, regulatory factors, working environment factors and management factors. The consequences of information distortion in EHRs included threats to patient safety, poor operational performance, eroded trust, compromised research quality and health inequity.
conclusionThis concept analysis enhances the understanding of information distortion in EHRs and provides a foundation for further empirical validation. The findings may contribute to the development of measurement instruments and strategies to mitigate information distortion in healthcare settings. IMPACT: By undertaking a concept analysis of information distortion in EHRs, healthcare professionals will be better equipped to recognise and assess this ethical phenomenon, thereby supporting the development of targeted interventions to mitigate potential harms to healthcare practices. In addition, the clarity of this concept could provide a new angle from which to analyse the origins of flawed EHR documentation and its ripple effects across healthcare systems. PATIENT OR PUBLIC CONTRIBUTION: No patient or public involvement.
Indexed as
Identifiers
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.