ArticleScientific reports2025
Data-driven FMEA approach for hazard identification and risk evaluation in digital health.
Article in Scientific reports, 2025. 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
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
1 citing paper in PubMed.
- AI-Induced Cybersecurity Risks in Healthcare: A Narrative Review of Blockchain-Based Solutions Within a Clinical Risk Management Framework.Risk management and healthcare policy · 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
7 authors.
Funding
Abstract
The increasing digitization of healthcare data systems presents substantial opportunities for enhancing patient care and operational efficiency, while simultaneously introducing critical vulnerabilities such as unauthorized access, inconsistent data formats, and privacy breaches. To systematically address these risks, this study employs Failure Modes and Effects Analysis (FMEA) to identify, evaluate, and prioritize potential hazards within digital healthcare systems. It is among the first to apply the FMEA approach in a comprehensive manner to assess risks across diverse healthcare data categories and modalities, offering a novel perspective on the vulnerabilities inherent in digital health systems. Through a structured methodology, this research investigates risks across three key healthcare data categories, such as clinical, operational, and patient-reported, as well as across five major data modalities including text, image, tabular, audio, and video. Each identified failure mode was assessed through expert consultation and comprehensive literature review, considering its severity, occurrence, and detectability, and subsequently assigned a Risk Priority Number for quantitative prioritization. Key findings highlighted significant risks, including unauthorized access, data corruption, transmission errors, and privacy breaches, that threaten patient safety and system reliability. This study provides actionable recommendations to strengthen data integrity, security, and interoperability, supporting the safe adoption of AI, blockchain, and other emerging technologies in developing secure and resilient digital healthcare systems.
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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.