一种自动自适应的数字健康干预措施,以减少与阿片类药物相关的风险,同时节省顾问的时间:基于人工智能和患者报告的风险指标的治疗决策的定量分析
John D Piette1,2, Laura Thomas1,3, Sean Newman1,2
1Ann Arbor Department of Veterans Affairs Center for Clinical Management Research, Ann Arbor, MI, United States.
Journal of medical Internet research
|July 11, 2023
概括
这项研究表明,强化学习 (RL) 可以针对疼痛患者滥用阿片类止痛药 (OA) 的数字健康干预措施进行个性化. 由人工智能驱动的系统减少了OA滥用行为,同时优化了辅导员的时间.
科学领域:
- 医疗保健中的人工智能
- 数字健康干预措施 数字健康干预措施
- 强化学习的应用 强化学习的应用
背景情况:
- 类阿片类止痛药 (OA) 存在诸如依赖和过量服用等风险.
- 由于不切实际的咨询要求,大规模降低OA的风险具有挑战性.
研究的目的:
- 评估强化学习 (RL) AI干预措施,以实现个性化的患者参与.
- 评估AI是否可以减少自我报告的阿片类药物滥用行为.
- 确定AI是否可以节省辅导员的时间.
主要方法:
- 数字健康干预 (PowerED) 使用RL为228名疼痛患者个性化每周的互动.
- 从简短/扩展的激励IVR电话或实时辅导员电话中选择的RL算法.
- 目标是尽量减少OA风险,并通过在有效时支持IVR来节省辅导员的时间.
主要成果:
- 随着时间的推移,RL干预适应了,大大减少了现场咨询会议.
- 这种调整导致了自我报告的OA风险得分的逐步改善.
- 基线风险最高的患者表现出特别明显的改善.
结论:
- 强化学习有效地确定了减少阿片类药物滥用行为的最佳治疗方法.
- 由RL支持的数字健康干预为管理OA处方提供了一个可扩展的解决方案.
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