一个基于计算机的,付费者主导的,低强度的多道教育活动降低了医疗保险退院后的利用率
Danica Fernandes1, Elise Kokonas2, Jai Bansal1
1Analytics & Behavior Change, Aetna, CVS Health, New York, US.
JMIR human factors
|March 31, 2025
概括
一个多道教育活动显著减少了医疗保险优势会员的30日和90天的住院再入院和急诊室访问. 这种可扩展的干预使用了行为改变技术来改善患者的健康结果.
科学领域:
- 医疗信息学 医疗信息学
- 行为科学 行为科学
- 公共卫生 公共卫生
背景情况:
- 避免再接收是一个优先事项,但目前的干预措施往往是昂贵的和不可持续的.
- 国家付款人可以使用数据来识别针对性干预的高风险成员.
- 这项研究侧重于可扩展的,以行为改变为重点的教育干预措施.
研究的目的:
- 评估一项以信息技术为导向的多道教育活动对减少再录取和ED访问的影响.
- 评估针对医疗保险优势会员量身定制的行为改变技术的有效性.
- 为了确定低强度教育干预措施的可扩展性,用于退院后的护理.
主要方法:
- 一个质量改进计划使用以人为中心的设计和行为改变原则.
- 一个基于证据的宣传活动提供了教育,赋能和说服力.
- 成员 (N=368,393) 通过使用数据识别,并分配到标准或增强的消息群组.
主要成果:
- 加强外展活动显著减少了30天的 readmissions (-4.1%) 和ED访问 (-3.4%).
- 此外,这项干预措施还使90天的再接收率 (-5.4%) 和ED访问率 (-3.8%) 显著减少.
- 这些积极的结果在美国各地的医疗保险优势会员中观察到.
结论:
- 低强度,多道的教育宣传有效地减少了可避免的再录取和ED访问.
- 行为改变技术对于修改最近出院的患者的健康行为至关重要.
- 这一策略对于Medicare Advantage会员,尤其是65岁以上的会员来说尤其有效.
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