Abstract

Objective

Artificial intelligence (AI) and machine learning (ML) enabled healthcare is now feasible for many health systems, yet little is known about effective strategies of system architecture and governance mechanisms for implementation. Our objective was to identify the different computational and organizational setups that early-adopter health systems have utilized to integrate AI/ML clinical decision support (AI-CDS) and scrutinize their trade-offs.

Materials and Methods

We conducted structured interviews with health systems with AI deployment experience about their organizational and computational setups for deploying AI-CDS at point of care.

Results

We contacted 34 health systems and interviewed 20 healthcare sites (58% response rate). Twelve (60%) sites used the native electronic health record vendor configuration for model development and deployment, making it the most common shared infrastructure. Nine (45%) sites used alternative computational configurations which varied significantly. Organizational configurations for managing AI-CDS were distinguished by how they identified model needs, built and implemented models, and were separable into 3 major types: Decentralized translation (n = 10, 50%), IT Department led (n = 2, 10%), and AI in Healthcare (AIHC) Team (n = 8, 40%).

Discussion

No singular computational configuration enables all current use cases for AI-CDS. Health systems need to consider their desired applications for AI-CDS and whether investment in extending the off-the-shelf infrastructure is needed. Each organizational setup confers trade-offs for health systems planning strategies to implement AI-CDS.

Conclusion

Health systems will be able to use this framework to understand strengths and weaknesses of alternative organizational and computational setups when designing their strategy for artificial intelligence.

中文翻译：

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对在卫生系统中部署预测模型的现有组织和计算设置的调查

摘要

客观的

人工智能 (AI) 和机器学习 (ML) 支持的医疗保健现在对许多卫生系统都是可行的，但对系统架构的有效策略和实施治理机制知之甚少。我们的目标是确定早期采用者卫生系统用于集成 AI/ML 临床决策支持 (AI-CDS) 的不同计算和组织设置，并仔细审查它们的权衡。

材料和方法

我们对具有 AI 部署经验的卫生系统进行了结构化访谈，了解他们在护理点部署 AI-CDS 的组织和计算设置。

结果

我们联系了 34 个卫生系统并采访了 20 个医疗机构（回复率为 58%）。十二个 (60%) 站点使用本地电子健康记录供应商配置进行模型开发和部署，使其成为最常见的共享基础架构。九个 (45%) 站点使用了显着变化的替代计算配置。管理 AI-CDS 的组织配置以其识别模型需求、构建和实施模型的方式区分，可分为 3 种主要类型：分散式翻译（n = 10, 50%）、IT 部门领导（n = 2, 10%） ) 和 AI 医疗保健 (AIHC) 团队（n = 8, 40%）。

讨论

没有单一的计算配置支持 AI-CDS 的所有当前用例。卫生系统需要考虑他们对 AI-CDS 的期望应用，以及是否需要投资扩展现成的基础设施。每个组织设置都会为实施 AI-CDS 的卫生系统规划策略进行权衡。

结论

在设计人工智能战略时，卫生系统将能够使用该框架来了解替代组织和计算设置的优缺点。