研究AI如何帮助资源有限的非营利医疗组织提升协作与决策能力
AI-Enabled Knowledge Sharing for Enhanced Collaboration and Decision-Making in Non-Profit Healthcare Organizations: A Scoping Review Protocol
- 结合三大理论框架，分析AI在组织中的双重作用
- 聚焦美国国际开发署停援后的情境，识别技术适配关键点
- 为非营利医疗机构设计轻量化AI方案提供依据
本协议提出一项系统性范围综述，旨在梳理资源受限的非营利医疗组织中人工智能赋能知识共享的现有证据。研究聚焦于美国国际开发署（USAID）项目终止后外部支持减少的背景下，此类技术如何增强协作与决策能力。基于资源基础观、动态能力理论和吸收能力理论三个理论框架，探讨AI作为战略资源及组织学习与敏捷性的推动者角色。采用PRISMA-ScR指南，通过多数据库系统检索、明确纳入排除标准与结构化数据提取流程，实现方法严谨性。该综述将整合理论与实证证据，识别文献空白，为非营利医疗环境中高效、低成本的AI解决方案设计提供参考。
原文摘要 · Abstract (English)
This protocol outlines a scoping review designed to systematically map the existing body of evidence on AI-enabled knowledge sharing in resource-limited non-profit healthcare organizations. The review aims to investigate how such technologies enhance collaboration and decision-making, particularly in the context of reduced external support following the cessation of USAID operations. Guided by three theoretical frameworks namely, the Resource-Based View, Dynamic Capabilities Theory, and Absorptive Capacity Theory, this study will explore the dual role of AI as a strategic resource and an enabler of organizational learning and agility. The protocol details a rigorous methodological approach based on PRISMA-ScR guidelines, encompassing a systematic search strategy across multiple databases, inclusion and exclusion criteria, and a structured data extraction process. By integrating theoretical insights with empirical evidence, this scoping review seeks to identify critical gaps in the literature and inform the design of effective, resource-optimized AI solutions in non-profit healthcare settings.
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