人工智能增强的交换中心,以促进基于证据的决策和社会支出:一个概念框架
Pamela R Buckley1,2,3, Diana Fishbein4,5,6, Neil J Wollman1
1National Prevention Science Coalition to Improve Lives (NPSC), Chapel Hill, NC, USA.
概括
这项研究提出了一个人工智能驱动的平台,以改善社会支出决策. 它增强了基于证据的决策,以实现有效,公平和文化相关的预防干预.
科学领域:
- 公共卫生 公共卫生
- 医疗保健服务研究 医疗服务研究
- 基于证据的实践.
背景情况:
- 社会支出将资金用于弱势群体,需要基于证据的决策.
- 目前基于证据的预防性干预 (EBPI) 的在线清算中心在手册更新和实施支持方面存在局限性.
- 需要有反应的平台来有效地传播公平的,与文化相关的EBPI.
研究的目的:
- 提出一个先进的在线清算中心平台的概念框架.
- 加强有效,公平和文化相关的EBPI的识别和扩展.
- 在公共卫生方面的社会支出中支持基于证据的决策.
主要方法:
- 一个分为两部分的概念框架,用于整合人工智能和人类监督的清算中心平台.
- 实施"生活"审查,以自动更新评估.
- 整合一个聊天机器人,提供EBPI评估,实施和建议的指导.
主要成果:
- 拟议的平台将自动更新评估,确保当前的证据.
- 一个聊天机器人将提供EBPI,公平结果,文化相关性,实施支持和成本的建议.
- 该平台旨在加快EBPI的翻译和扩展.
结论:
- 开发的平台将提供关于有效预防策略的全面和可访问的证据.
- 它将在道德上加快EBPI的识别和扩展.
- 这种方法支持改善社会支出和人口健康结果.
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