Presentation (CTI)

OBJECTIVES: This study aimed to develop and validate a Materiovigilance Causality Assesment Tool, enhanced by an integrated AI-driven Dashboard, to improve the monitoring and analysis of device-related issues in the future.
METHODS: A comprehensive literature review informed the development of a data collection protocol. A pilot study involving 30% of the target population (N=100) assessed the feasibility of the tool, which initially comprised 12 items. Content validity was evaluated by a panel of 12 experts, leading to modifications that resulted in a final tool of 11 items scored on a five-point Likert scale. An AI-driven Dashboard will be developed to visualize data trends, automate scoring, and facilitate real-time analysis. The tool was then applied to 70 cases, with scores calculated to categorize cases as CERTAIN, POSSIBLE, PROBABLE or UNLIKELY based on responses.
RESULTS: The final application of the tool showed that 90% of cases related to IV cannulas were categorized as PROBABLE, while 10% comes under certain. While 14 out of 23 cases of Foley catheters were also deemed PROBABLE and remaining were UNLIKELY. The tool demonstrated strong content validity (Aiken’s V = 0.85) and reliability (Cronbach’s alpha ranging from 0.68 to 0.77).
CONCLUSIONS: The Materiovigilance Causality Assesment Tool, complemented by an AI-driven Dashboard, will be a validated instrument that effectively identifies potential adverse events associated with medical devices. Its implementation will significantly enhance patient safety and the overall quality of healthcare delivery.