选择最好的放射学工作流效率应用程序
Prateek Bharadwaj1, Michael Berger2, Steven L Blumer1
1Bayer AG, Berlin, Germany.
Journal of imaging informatics in medicine
|June 14, 2024
概括
在数字放射学中选择AI工作流效率应用是具有挑战性的. 这篇文章提供了一个框架来评估解决方案,确定31个痛点,并量化放射学实践的财务利益.
科学领域:
- 放射学 信息学 信息学
- 医疗信息学 医疗信息学
- 医学成像技术 医学成像技术
背景情况:
- 数字放射学领域已经看到人工智能 (AI) 和工作流效率应用的快速增长.
- 超过500个人工智能应用程序已经获得了FDA的510 (k) 许可,以及许多不受监管的工具.
- 这些工具为改善全球放射科部门的运营效率提供了巨大的潜力.
研究的目的:
- 为评估放射学工作流效率应用提供一个结构化的方法.
- 识别关键的操作痛点,并将它们映射到可用的应用程序功能.
- 引入一个评估和选择最有效解决方案的框架.
主要方法:
- 在放射学工作流程中识别和分类31个关键的操作痛点.
- 开发一个评估框架,评估疼痛点覆盖率,效率要求,证据,集成和可用性.
- 对市场应用的大规模分析,将综合生态系统与利基解决方案区分开来.
主要成果:
- 提出了一个框架,以指导工作流效率应用程序的选择.
- 分析区分了广泛的解决方案和有针对性的解决方案.
- 提出了一种量化财务收益和投资回报的方法.
结论:
- 客观的评估标准对于在复杂的放射学工作流效率工具领域进行导航至关重要.
- 拟议的框架有助于放射学实践对应用程序的采用做出明智的决定.
- 量化财务效益支持对效率技术的战略投资.
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