利用生成性人工智能来模拟精神卫生保健的获取和利用情况
Cortney VanHook1, Daniel Abusuampeh2, Jordan Pollard3
1School of Social Work, University of Illinois at Urbana-Champaign, Urbana, IL, United States.
Frontiers in health services
|September 11, 2025
概括
生成型人工智能可以模拟心理健康旅程,以改善不同人群的护理准入和结果. 这种方法有助于识别障碍,并为更公平的医疗保健提供个性化的干预措施.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 医疗保健中的人工智能
- 心理健康信息学心理健康信息学
背景情况:
- 获得心理健康护理是复杂的,受到个人,文化和系统因素的影响.
- 现有的评估接入和护理提供模式在捕捉现实世界的细微差别方面存在局限性.
- 基于测量的护理 (MBC) 提供了一种数据驱动的方法来个性化心理健康治疗.
研究的目的:
- 检查生成性人工智能 (AI) 如何模拟和增强心理健康护理旅程.
- 支持改善不同个体在心理健康服务中的获取,个性化和结果.
- 将人工智能与既定访问模型和MBC集成为新型护理途径框架.
主要方法:
- 开发了一个由人工智能生成的多样化个体 (马库斯·约翰逊) 的案例研究.
- 整合了安德森的行为模型,Penchansky和Thomas的访问维度,以及MBC.
- 模拟了影响心理健康护理获取的因素的相互作用,并确定了干预机会.
主要成果:
- 与MBC结合的生成AI模拟可以复制心理健康护理中复杂的现实场景.
- 人工智能模拟为临床决策提供信息,通过持续评估和规划实现个性化干预.
- 远程医疗,灵活的安排和文化能力对于克服接入障碍和改善结果至关重要.
结论:
- 生成性人工智能为理解和改善心理健康护理的获取和提供提供提供了一个新的框架.
- 这种方法使教育工作者,临床医生和设计师能够应对多方面的访问挑战.
- 人工智能促进了对护理策略的无风险测试,促进了更公平,更有效的心理健康系统.
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