Socio-Technical Blueprint for Implementing Predictive AI Clinical Decision Support in Multi-Site US Healthcare Groups
在美国多院区医疗集团内实施预测型临床决策支持智能体AI 的社会技术蓝图 - ByDrug
Summary
This article addresses the challenges of traditional static Clinical Decision Support Systems (CDSS) within multi-site U.S. healthcare groups, such as functional fragmentation and difficulty integrating heterogeneous systems. It proposes a comprehensive implementation roadmap based on socio-technical integration to guide AI utilization.
Details
Focusing on application cases in multi-site US healthcare groups, this article identifies pain points of traditional static CDSS, including functional silos and poor compatibility with diverse systems. The proposed solution is a comprehensive implementation blueprint utilizing a 'socio-technical' perspective, going beyond mere technological deployment. Key features include the integration of three dimensions: ① AI technology architecture, ② hospital organizational management, and ③ ethical/legal supervision. Specific technical elements mentioned are FHIR standardization interoperability and multi-agent collaborative scheduling. Furthermore, compliance with FDA medical software regulations is considered. The proposal was developed based on practical validation using Santa Clara Medical Group's 12 facilities and verified through mixed research methods. It aims to move beyond the technology-centric view of AI research by providing a practical framework that includes organizational and human elements. Predictive AI is defined by five core characteristics: autonomous operation, proactive prediction, continuous learning, multi-agent collaboration, and goal-oriented dynamic planning.
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