"患者不是数据集":通过自动化,负责任地产生投资回报
Aaron Miri1,2, Michael A Mayo1,2
1Aaron Miri, DHA, FCHIME, CHCIO,is executive vice president, chief digital and information officer at Baptist Health in Jacksonville, Florida.
Frontiers of health services management
|November 26, 2025
概括
医疗保健中的人工智能 (AI) 通过数据货币化提供了效率和成本降低. 负责任的实施需要对所有利益相关者进行仔细的数据隐私,合规性和风险评估.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 医学中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 随着医疗保健成本的上升和患者需求的增加,需要技术创新.
- 卫生系统正在投资人工智能,机器学习和深度学习,以提高效率和结果.
- 现有的投资加强了患者数据收集,研究和治疗开发.
研究的目的:
- 探索患者数据对人工智能培训的负责任利用.
- 检查AI在数据货币化,加快护理和降低成本方面的潜力.
- 解决人工智能在医疗保健中的风险和意外后果.
主要方法:
- 对当前医疗保健技术投资的分析.
- 评估人工智能模型,包括机器学习和生成人工智能.
- 对数据隐私,合规性和风险管理策略的审查.
主要成果:
- 人工智能的采用改善了访问,结果和数据标准化.
- 通过数据货币化和运营效率提高,有可能获得显著的ROI.
- 确定了对负责任的数据使用和风险评估的关键需求.
结论:
- 医疗保健正处于人工智能集成的关键时刻.
- 负责任的数据使用,隐私和合规性至关重要.
- 需要明确的战略来衡量人工智能对利益相关者的影响.
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