Scholarlink

<p>Artificial intelligence (AI) promises major advances in healthcare but often fails to scale due to workflow misalignment, data gaps, and clinician mistrust. Existing AI frameworks emphasize technical development, validation, or governance but offer limited guidance for real-world operationalization. We propose a concise, practice-oriented 10-step quality improvement (QI) roadmap to support safe, scalable AI implementation across healthcare settings. Grounded in QI and implementation science, the roadmap guides leaders from problem selection and data readiness through iterative piloting, evaluation, and system-wide scale-up. By treating AI as a continuous improvement intervention rather than a standalone technology, this framework supports sustainable adoption and measurable improvements in care delivery, equity, and system performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A quality improvement roadmap for scalable AI implementation in healthcare

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月27日

查看全文
  • Angel Arnaout,
  • Bethina Abrahams,
  • Pamela Hinada,
  • Michael Mckenzie

摘要

Artificial intelligence (AI) promises major advances in healthcare but often fails to scale due to workflow misalignment, data gaps, and clinician mistrust. Existing AI frameworks emphasize technical development, validation, or governance but offer limited guidance for real-world operationalization. We propose a concise, practice-oriented 10-step quality improvement (QI) roadmap to support safe, scalable AI implementation across healthcare settings. Grounded in QI and implementation science, the roadmap guides leaders from problem selection and data readiness through iterative piloting, evaluation, and system-wide scale-up. By treating AI as a continuous improvement intervention rather than a standalone technology, this framework supports sustainable adoption and measurable improvements in care delivery, equity, and system performance.