텍스트 네트워크 분석을 통한 환자중앙감시시스템의 사용적합성 평가를 위한 위해요인 분석
Hazard Analysis for Usability Evaluation of Central Monitoring System through Text Network Analysis
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초록
In this study, text network analysis was performed using PMS(Post-Marketing Surveillance) data collected from the FDA's MAUDE(Manufacturer and User Facility Device Experience) database to evaluate the usability of the central monitoring system. Based on the data reported from January 1, 2021 to June 30, 2023, keywords related to the central moni- toring system were extracted and visualized with a text network. By analyzing the eigenvector centrality of text network, we identified hazards and types of hazardous situations related to usability of the central monitoring system. Eigenvector centrali- ty was chosen because it is relatively more accurate than other centralities. In addition, we derived an appropriate use scenario to evaluate the usability of the central monitoring system. The research results provide more realistic and valuable insights through data derived based on actual adverse event cases, and are expected to contribute to improving safety and reliability by identifying user requirements for improved usability and reducing use errors in the future.
Abstract
In this study, text network analysis was performed using PMS(Post-Marketing Surveillance) data collected from the FDA's MAUDE(Manufacturer and User Facility Device Experience) database to evaluate the usability of the central monitoring system. Based on the data reported from January 1, 2021 to June 30, 2023, keywords related to the central moni- toring system were extracted and visualized with a text network. By analyzing the eigenvector centrality of text network, we identified hazards and types of hazardous situations related to usability of the central monitoring system. Eigenvector centrali- ty was chosen because it is relatively more accurate than other centralities. In addition, we derived an appropriate use scenario to evaluate the usability of the central monitoring system. The research results provide more realistic and valuable insights through data derived based on actual adverse event cases, and are expected to contribute to improving safety and reliability by identifying user requirements for improved usability and reducing use errors in the future.
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