ArticleJMIR formative research2026
A Network Visualization Query System for Multidrug Compatibility Based on a WeChat Mini Program: Preliminary Usability and Efficiency Evaluation.
Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Intravenous drug incompatibility is a significant medication safety hazard, particularly in complex multidrug regimens. Traditional text-based, pairwise query methods are inefficient and impose a substantial cognitive load on clinicians. While network visualization has the potential to address these challenges, its application in drug compatibility queries remains underexplored. Objective: This study aimed to design, develop, and evaluate a novel drug compatibility query system based on a WeChat Mini Program. The system integrates diverse data sources and uses network visualization to present complex compatibility relationships. We sought to empirically assess its impact on efficiency and user experience. Methods: A preliminary crossover usability and efficiency evaluation was conducted. Phase 1 involved the construction of a drug compatibility knowledge base from authoritative handbooks and drug labels, and the development of the query system. Phase 2 comprised a system evaluation with 36 pharmacists, 6 physicians, and 5 nurses. The evaluation included a scenario-based task completion time analysis comparing the system (Mode A) with traditional print-based resources (Mode B), a quality assessment using the Mobile Application Rating Scale (MARS), and structured posttask user feedback to gather insights on user experience. Results: The query system demonstrated a substantial reduction in task completion time, with median time savings of 2.85 (IQR 1.98-4.15) minutes, 5.45 (IQR 3.95-7.20) minutes, and 31.2 (IQR 27.5-35.1) minutes, respectively, in 3 scenarios with different complexity. The system received a high mean overall quality score of 3.88 (SD 0.35) on the MARS. The functionality dimension scored the highest (mean 4.21, SD 0.51), while engagement scored the lowest (mean 3.21, SD 0.62). Posttask structured feedback revealed five major areas of user feedback: (1) baseline experience and first impressions, (2) user experience with network visualization, (3) perceived efficiency and cognitive load, (4) trust and information quality, and (5) future application considerations. Users praised the system's efficiency and intuitive design but expressed a strong need for transparent data sources and management advice to build trust. Conclusions: A query system based on network visualization demonstrates potential to support the efficiency of multidrug compatibility queries within this preliminary evaluation. It may mitigate cognitive load and offer an at-a-glance understanding of complex drug relationships. However, formal clinical accuracy validation remains a mandatory precondition before bedside clinical deployment is considered.
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