ArticleFrontiers in digital health2026
A qualitative, multi-framework methodology for analysing health information technology-related patient safety incidents.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Usability and Workflow Integration of a Machine Learning-Derived Neonatal Risk Predictor in Kenyan Neonatal Units: Multisite User-Centered Pilot Evaluation.JMIR human factors · 2026Article
- Understanding the Limits of Patient Safety Classification Systems for Health Information Technology-Related Incidents.JMIR human factors · 2026Article
- Governance pathways for scaling neonatal risk stratification tools in LMIC health systems: a multi-site qualitative study in Kenya.Frontiers in digital health · 2026Article
- Governance and implementation of solar-powered digital health systems in low- and middle-income countries: a mixed-methods study from Kenya and Ethiopia.Frontiers in digital health · 2026Article
- Toward a mixed-methods framework for evaluating digital health systems in low-resource settings: an infrastructure-aware perspective.Frontiers in digital health · 2026Article
- Patient misidentification and data integrity risks in ultrasound imaging: a case report using multi-framework incident analysis.Frontiers in health services · 2026Article
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Abstract
Background: The increasing reliance on health information technology (HIT) has introduced new and often unforeseen risks to patient safety in complex healthcare systems. Many HIT-related safety problems emerge only after systems are embedded in routine clinical practice and are difficult to identify using prospective or purely quantitative methods. Incident reports provide valuable insights into real-world failures, but systematic methodologies for analysing HIT-related incidents remain underdeveloped. Objective: This article aims to describe and formalise a qualitative, multi-framework methodology for analysing health information technology-related patient safety incidents, based on retrospective incident report data. Methods: The methodology integrates multiple data sources, including incident reporting systems, existing incident databases, and supplementary interview-derived narratives. HIT-related incidents are identified through a structured screening process combining keyword-based searches and manual narrative review. Analysis is conducted using complementary deductive and inductive approaches, including established patient safety classification systems, HIT-specific frameworks, workflow-based analysis, and thematic analysis. Structured coding procedures, independent review, consensus-building, and reflexive practices are employed to enhance analytical rigour. Findings are systematically translated into preventive and corrective strategies grounded in sociotechnical principles. Results: The proposed methodology enables systematic identification and characterisation of HIT-related patient safety incidents, capturing sociotechnical mechanisms, contributing factors, and outcomes that are not readily identified through single analytical frameworks. By combining multiple perspectives, the approach supports analysis of low-frequency, high-impact events, workflow disruptions, and system-level failures, and facilitates the development of context-sensitive preventive and corrective strategies. Conclusions: This multi-framework qualitative methodology provides a structured, transferable approach to learning from HIT-related patient safety incidents in complex healthcare systems. The framework supports researchers, clinicians, and safety analysts in understanding how digital systems fail in real-world practice and offers a robust foundation for improving the safety and resilience of digital healthcare.
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