开发一种混合自动图表检查,数据收集和分析系统
Edward L Clouser1, Quan Chen1, Daniel P Harrington1
1Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA.
Journal of applied clinical medical physics
|July 14, 2025
概括
为瘤学开发了一个新的混合自动图表审查平台. 该系统简化了图表检查,提高了效率,并识别了放射治疗计划中的错误.
科学领域:
- 医学物理 医学物理
- 辐射瘤学 辐射瘤学
- 医疗信息学 医疗信息学
背景情况:
- 放射治疗需要严格的图表检查,以确保患者的安全和治疗的准确性.
- 手动图表审查流程可能耗时且容易出现人为错误.
- 将自动化集成到图表审查中可以提高效率和一致性.
研究的目的:
- 描述混合自动图表审查平台的开发和临床实施.
- 创建一个综合系统,结合数据库,瘤信息系统 (OIS) 接口和用户界面 (UI).
主要方法:
- 制定了一份共识检查清单,以标准化临床物理工作人员和住院人员的实践.
- 引入了"计划属性"的概念,用于专门的,自动化的检查列表生成.
- 实施了混合自动化方法,将自动化步骤与人类审查相结合.
- 建立日志并对发现的缺陷进行分类.
主要成果:
- 分析了超过4年的数据 (2020年10月起) 涉及427,073次测试和7,882次图表检查.
- 确定了7116个小错误,679个重大错误和71个计划拒绝.
- 该平台证明了其在检测需要计划补救的重大缺陷方面的能力.
结论:
- 成功开发并实施混合自动图表检查程序.
- 该软件通过现代数据库和软件设计来增强每个案例的性能和用户体验.
- 图表检查数据库有助于高层审查和改进计划检查和用户性能.
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