From Challenge to Studio: A Prototype Frontline AI Innovation Mechanism to Address the AI Embedding Deficit in Healthcare and Educational Institutions

IF 2.3 Q2 HEALTH POLICY & SERVICES

Learning Health Systems Pub Date : 2026-10-09 DOI:10.1002/lrh2.70126

Bo Xie

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引用次数: 0

Abstract

Introduction

Health systems are expanding access to artificial intelligence (AI), yet frontline integration into teaching, clinical workflows, and research practice remains uneven. We frame this gap as the “AI embedding deficit,” reflecting limited organizational pathways that enable frontline professionals to translate local needs into legitimate, supported, and sustainable AI-enabled tools and workflows. This Experience Report describes a prototype intervention mechanism implemented at an academic nursing school: a rapid-cycle Nursing-AI Challenge followed by a year-round Nursing-AI Studio (NAIS) designed to stabilize and scale embedding.

Methods

We implemented the Nursing-AI Challenge as a structured, time-bounded mechanism to solicit real-world challenges from nursing faculty, translate and curate submissions through an interdisciplinary facilitation role, match challenges to AI trainees and mentors, and support rapid proposal development culminating in a public showcase. We then developed NAIS to address continuity and embedding barriers revealed in the Challenge, incorporating continuous intake, environmental scanning, supported co-development, year-round coordination, formalized interdisciplinary partnerships, and a strategic memorandum of understanding with the Chief Information Officer's office to support legitimate integration pathways.

Results

In the inaugural Challenge cycle (Spring 2025), nursing faculty identified 17 challenges spanning education, clinical practice, and research, and students in AI-related fields submitted eight proposals, followed by team formation, co-design of the AI prototypes, and a public showcase event. Implementation reflections identified key sustainability barriers including semester-bounded participation, summer momentum gaps, and intellectual property ambiguity. NAIS was established (Fall 2025 onward) to mitigate these barriers and support ongoing embedding. In early Spring 2026, NAIS launched “Teaching with AI,” a clinical faculty innovation initiative piloting an AI-powered teaching tool fully deployed within the university's information technology infrastructure.

Conclusions

A Challenge-to-Studio pathway may function as a practical prototype mechanism for learning health systems seeking to reduce embedding barriers and convert decentralized frontline innovation into durable, governable, and evaluable practice change.

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从挑战到工作室：解决医疗和教育机构人工智能嵌入赤字的原型前线人工智能创新机制

卫生系统正在扩大人工智能（AI）的使用范围，但一线融入教学、临床工作流程和研究实践的情况仍然不均衡。我们将这一差距定义为“人工智能嵌入赤字”，反映了一线专业人员能够将当地需求转化为合法、受支持和可持续的人工智能工具和工作流程的组织途径有限。本经验报告描述了在一所学术护理学校实施的一个原型干预机制：一个快速周期的护理人工智能挑战，随后是一个旨在稳定和扩大嵌入的全年护理人工智能工作室（NAIS）。我们将护理人工智能挑战作为一种结构化的、有时间限制的机制来实施，以征求护理教师提出的现实世界的挑战，通过跨学科的促进作用翻译和策划提交的内容，将挑战与人工智能学员和导师相匹配，并支持快速的提案发展，最终形成公开展示。然后，我们开发了NAIS，以解决挑战中暴露的连续性和嵌入障碍，包括持续吸收、环境扫描、支持的共同开发、全年协调、正式的跨学科伙伴关系，以及与首席信息官办公室签署的战略谅解备忘录，以支持合法的整合途径。在首届挑战赛周期（2025年春季）中，护理学院确定了17项挑战，涵盖教育、临床实践和研究，人工智能相关领域的学生提交了8份提案，随后组建团队，共同设计人工智能原型，并举行了公开展示活动。实施反思确定了主要的可持续性障碍，包括学期参与、夏季动力差距和知识产权模糊性。NAIS成立于2025年秋季，旨在缓解这些障碍并支持正在进行的嵌入。在2026年早春，NAIS推出了“人工智能教学”，这是一项临床教师创新计划，试点在大学的信息技术基础设施中全面部署人工智能教学工具。从挑战到工作室的途径可以作为一种实用的原型机制，用于学习卫生系统，寻求减少嵌入障碍，并将分散的一线创新转化为持久、可治理和可评估的实践变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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