通过电子健康记录通过自动化偶然发现通知,使用语音宏
Tianyuan Fu1, Sheila Berlin2, Amit Gupta2
1University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Avenue, BSH 5056, Cleveland, OH, 44106, USA. tianyuan.fu@uhhospitals.org.
实施可操作的偶然发现 (AIFs) 通信工作流程,使用电子健康记录 (EHR) 语音宏改进了放射学报告的质量和临床医生的发现. 该系统为多医院环境中的偶然发现提供了及时的后续订单.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗信息学 医疗信息学
- 临床工作流的优化 临床工作流优化
背景情况:
- 放射学报告中的偶然发现对及时的临床沟通和后续工作提出了挑战.
- 现有的通信方法可能是低效的,导致患者护理的延迟.
研究的目的:
- 实施和评估一个可操作的偶然发现 (AIF) 通信工作流程,集成到电子健康记录 (EHR).
- 提高放射学报告的质量,并简化向临床医生提供偶然发现的流程.
- 为了促进随机发现后续成像检查的订单.
主要方法:
- 开发了基于ACR质量指标的各种器官系统的标准化语音宏.
- 将宏集成到EHR (Epic) 中,用于自动临床医生通知和跟踪.
- 监控警报确认73小时内通过放射学操作.
主要成果:
- 在2023年9月至2024年3月期间,对10,766名患者提交了超过12,919个AIF警报.
- 警报的中位确认时间为8.1小时,93.9%的警报在73小时内得到确认.
- 对62.3%的患者进行了随访成像,这些患者被确定为AIFs.
结论:
- 使用EHR指令宏的标准化AIF通信工作流程有效地促进了调查结果和后续建议的交付.
- 这种方法提高了放射学报告的质量,并改善了偶然发现的临床管理.
- 实施的系统显示了高的合规性和高效的沟通,在一个多医院的学术卫生系统.
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