Background The adverse event report of medical devices is one of the postmarket surveillance tools used by regulators to monitor device performance, detect potential device-related safety issues, and contribute to benefit-risk assessments of these products. However, with the development of the related technologies and market, the number of adverse events has also been on the rise, which in turn results in the need to develop efficient tools that help to analyze adverse events monitoring data and to identify risk signals. Objective This study aimed to establish a hazard classification framework of medical devices and to apply it over practical adverse event data on infusion pumps. Subsequently, it aimed to analyze the risks of infusion pumps and to provide a reference for the risk management of this type of device. Methods The authors define a general hierarchical classification of medical device hazards. This classification is combined with the Trace Intersecting Theory to form a human-machine-environment interaction model. Such a model was applied to the dataset of 2001 to 2017 class I infusion pump recalls extracted from the Food and Drug Administration (FDA) website. This dataset does not include cases involving illegal factors. Results The proposed model was used for conducting hazard analysis on 70 cases of class I infusion pump recalls by the FDA. According to the analytical results, an important source of product technical risk was that the infusion pumps did not infuse accurate dosage (ie, over- or underdelivery of fluid) . In addition, energy hazard and product component failure were identified as the major hazard form associated with infusion pump use and as the main direct cause for adverse events in the studied cases, respectively. Conclusions The proposed human-machine-environment interaction model, when applied to adverse event data, can help to identify the hazard forms and direct causes of adverse events associated with medical device use.

Background:The adverse event report of medical devices is one of the post-market surveillance tools for regulators to monitor device performance, detect potential device-related safety issues, and contribute to benefit-risk assessments of these products. Along with the development of the related technologies and market, the amount of adverse events keeps increasing, which results in the need for efficient tools that help to analyze the adverse events monitoring data and to identify the risk signals. Objective: To establish a hazard classification framework of the medical devices, and to apply it over practical adverse event data regarding infusion pumps. Subsequently, to analyze the risks of infusion pumps, and to provide reference for the risk management of this type of device. Methods: The authors defines a general hierarchical classification of medical device hazards. This classification is combined with the Trace Intersecting Theory to form a human-machineenvironment interaction model. Such model is applied to the dataset of 2001 ～ 2017 class Ⅰ infusion pump recalls extracted from FDA website. This dataset does not include the cases caused by illegal factors, in order to reflect the risk signals of this type of device. Results:The proposed model is leveraged in the hazard analysis over 70 cases of class I infusion pump recalls by FDA. According to the analytical results, the "infusion pump dose not infuse accurate dosage (over or under delivery of fluid)" is identified to be an important source of product technical risk. The Energy hazard is the major hazard form for infusion pumps. The product component failure is the main direct cause for the studied cases.