使用机器学习来匹配客户和治疗提供者:评估临床质量和护理成本
Jennifer L Lee1, Chris Billovits2, Shih-Yin Chen2
1Lyra Health, Burlingame, CA, USA; Department of Pediatrics, Emory University, Atlanta, GA, USA.
概括
基于价值的机器学习算法用于将心理健康客户与提供者匹配,显著降低了20%的护理成本,而不会影响治疗结果. 这种方法优化了提供者与客户的配对,以改善心理健康服务的提供.
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
- 临床心理学 临床心理学
背景情况:
- 将个人与合适的精神卫生保健提供者相匹配是一个重大挑战.
- 机器学习 (ML) 模型通过分析各种数据点来优化这些配对.
研究的目的:
- 为了比较实用ML算法的有效性和成本效益与基于价值的ML算法,用于将心理治疗客户与提供者匹配.
- 评估这些算法对治疗结果和护理总成本的影响.
主要方法:
- 使用倾向得分匹配来创建可比的队列.
- 使用了两个算法:实用算法 (后勤和临床特征) 和基于价值的算法 (添加提供商结果和成本数据).
- 焦虑症状的改善和总护理费用在匹配后的队列中进行了评估.
主要成果:
- 这两种算法都在很大的效果尺寸下,对焦虑症状产生了统计学上显著的改善.
- 在实用 (71.74%) 和基于价值 (70.02%) 的算法组之间,焦虑的可靠改善或恢复率相似.
- 基于价值的算法组显示,护理总成本减少了20%,使用的治疗疗程更少.
结论:
- 基于价值的ML匹配算法,结合提供商性能和成本指标,可以增强提供商-客户端配对.
- 这种方法可能会降低医疗保健成本,而不会影响患者的治疗结果.
- 需要进一步的研究来证实这些ML算法在精神卫生保健匹配中的通用性.
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