使用人工智能的健康加速器计划的社会决定因素的内容分析:公共卫生从业人员的用例
Kelli DePriest1, John Feher Iii, Kailen Gore
1Author Affiliations: Research Triangle Institute: RTI International, Research Triangle Park, North Carolina (Dr DePriest, Mr Feher III, Ms Gore, Dr Glasgow, and Mr Chew); Association of State and Territorial Health Officials (ASTHO), Arlington, Virginia (Mr Grant); National Association of County and City Health Officials (NACCHO), Washington, District of Columbia (Mr Holtgrave); Centers for Disease Control and Prevention (CDC), Atlanta, Georgia (Dr Hacker).
Journal of public health management and practice : JPHMP
|February 26, 2025
概括
大型语言模型 (LLM) 显示出分析公共卫生文件的潜力,但需要谨慎使用. 虽然更快,但人工智能在数据抽象和合成中的准确性低于人类分析,有可能出现错误.
科学领域:
- 公共卫生信息学 公共卫生信息学
- 医疗保健中的人工智能
- 定性数据分析 数据分析.
背景情况:
- 公共卫生实践依赖于分析许多文件,如战略计划和计划评估的需求评估.
- 对这些定性数据源的手动内容分析是耗时的,限制了实时程序改进.
- 大型语言模型 (LLM) 为自动化和加快这种分析提供了潜在的解决方案.
研究的目的:
- 评估使用LLM来分析公共卫生文件的可行性.
- 将GPT-4o的内容分析与人类分析的准确性和效率进行比较.
- 评估从社区健康改善计划中抽象和综合信息.
主要方法:
- 一个研究小组对4个社区健康改善计划进行了内容分析.
- 将GPT-4o的性能与研究小组对20个数据元素的结果进行了比较.
- 评估了资源需求,包括快速工程和错误纠正的时间.
主要成果:
- 在数据抽象方面,GPT-4o实现了79%的准确性,而研究小组的数据抽象率为94%,有伪造数据的情况.
- 对于合成,GPT-4o准确度为50%,在18个元素中有9个错误.
- 虽然GPT-4o最初更快,但当考虑到快速的工程和错误纠正时,整体时间节省会减少.
结论:
- 法律法规可以加快公共卫生文件的分析,但必须仔细考虑准确性.
- 公共卫生专业人员应该权衡潜在的资源节约与数据不准确的风险.
- 建议采取平衡的方法,将人工智能工具与人类监督相结合,以获得最佳的结果.
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