Background: AI-driven clinical decision support systems (CDSSs) hold promise for multidisciplinary team meetings (MDTMs). This study aimed to uncover the hurdles and aids in implementing CDSSs during breast cancer MDTMs. Methods: Twenty-four core team members from three hospitals engaged in semi-structured interviews, revealing a collective interest in experiencing CDSS workflows in clinical practice. All interviews were audio recorded, transcribed verbatim and analyzed anonymously. A standardized approach, ‘the framework method’, was used to create an analytical framework for data analysis, which was performed by two independent researchers. Results: Positive aspects included improved data visualization, time-saving features, automated trial matching, and enhanced documentation transparency. However, challenges emerged, primarily concerning data connectivity, guideline updates, the accuracy of AI-driven suggestions, and the risk of losing human involvement in decision making. Despite the complexities involved in CDSS development and integration, clinicians demonstrated enthusiasm to explore its potential benefits. Conclusions: Acknowledging the multifaceted nature of this challenge, insights into the barriers and facilitators identified in this study offer a potential roadmap for smoother future implementations. Understanding these factors could pave the way for more effective utilization of CDSSs in breast cancer MDTMs, enhancing patient care through informed decision making.
背景：人工智能驱动的临床决策支持系统（CDSS）在多学科团队会议（MDTM）中展现出应用潜力。本研究旨在揭示在乳腺癌MDTM中实施CDSS所面临的障碍与助力因素。方法：来自三家医院的24名核心团队成员参与半结构化访谈，结果显示临床工作者对体验CDSS工作流程具有普遍兴趣。所有访谈均进行录音、逐字转录并匿名分析。研究采用标准化“框架法”构建数据分析框架，由两名独立研究员完成分析工作。结果：积极方面包括改善的数据可视化功能、节省时间的特性、自动化临床试验匹配以及提升的文档记录透明度。然而，实施过程中也面临诸多挑战，主要集中在数据连通性、指南更新机制、人工智能建议的准确性，以及人类决策参与度可能降低的风险。尽管CDSS开发与整合过程复杂，临床医生仍展现出探索其潜在效益的积极意愿。结论：本研究通过识别实施过程中的障碍因素与促进因素，为未来更顺畅的CDSS实施提供了潜在路线图。深入理解这些多维度挑战，可为乳腺癌MDTM中更有效地运用CDSS指明方向，最终通过优化临床决策提升患者照护质量。
肿瘤(癌症)患者之家