实现初级保健中的数字多因素风险评估:总体审查和设计和实施建议
Lily C Taylor1, Niels Peek2, Ari Ercole3
1The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK lct46@medschl.cam.ac.uk.
BMJ health & care informatics
|March 3, 2026
概括
为初级保健开发有效的预测性数字健康工具,需要临床医生和公众共同设计. 建议强调了成功整合的14个特征,解决了诸如EHR互操作性等障碍.
科学领域:
- 数字健康数字健康
- 人工智能的人工智能是人工智能.
- 主要的护理是初级护理.
背景情况:
- 预测性数字健康和人工智能工具在初级保健中具有潜力.
- 实施面临重大障碍,包括系统集成的挑战.
研究的目的:
- 制定创建和整合预测性数字健康和人工智能工具在初级保健中的建议.
- 在初级保健机构中确定成功风险预测模型的关键特征.
主要方法:
- 一项综合性审查确定了风险预测工具的实施障碍.
- 一个共识阶段涉及利益相关者研讨会,调查和在线会议.
主要成果:
- 确定了15个实施障碍,特别是缺乏EHR集成和互操作性.
- 风险预测模型的14个核心特征被推,强调共同设计和数字基础设施集成.
结论:
- 早期的利益相关者和健康记录系统提供商的参与至关重要.
- 对最终用户需求的共同理解对于成功采用数字健康工具至关重要.
- 开发的个人资料指导了数字风险预测模型和其他初级保健创新的创建.
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