Data-driven FMEA approach for hazard identification and risk evaluation in digital health

Abstract

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.