产教融合背景下慢阻肺健康管理项目驱动的医学信息与人工智能项目化学习路径研究

摘 要：慢阻肺健康管理中的信息整理、风险沟通、人工复核与反馈记录，能够为医学信息与人工智能专业提供具有业务边界的项目化学习情境。本文定位为教学路径设计与实践思考，依据相关政策、项目化学习文献、健康数据治理要求和任务分析，将慢阻肺健康管理中的问题来源、数据条件、流程约束和交付要求转化为课程任务。在学生作为主要学习者的前提下，提出由任务进入、软件与数据实践、风险提示与人工复核、成果证据与项目反馈构成的学习路径，并提出临床协作人员参与问题界定、数据解释和边界复核的支持方式。路径以任务书、数据说明、过程记录、复核意见和版本材料作为学习证据，强调数据最小必要、原型的非临床使用、人工复核和责任留痕。本文不报告课程实施样本、学习效果、患者数据、模型性能或临床验证，所提出内容仅供课程设计与后续审慎实施参考。

关键词：产教融合；慢阻肺健康管理；项目化学习路径；医学信息与人工智能；医学人工智能教育

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Project-Based Learning Pathways for Medical Informatics and Artificial Intelligence Driven by a COPD Health-Management Project in the Context of Industry-Education Integration

Abstract: Information organisation, risk communication, human review and feedback records in COPD health management can be converted into bounded project-based learning tasks for medical informatics and artificial intelligence. This article is a teaching-pathway design and practice reflection rather than a report of a clinical or educational intervention. Drawing on policy documents, project-based learning literature, health-data governance requirements and task analysis, it translates problem sources, data conditions, workflow constraints and deliverable requirements into learning tasks. With students as the principal learners, the proposed pathway comprises task entry, software and data practice, risk-alert expression with human review, and evidence with project feedback. Clinical collaborators support question formulation, data interpretation and boundary review. Task briefs, data descriptions, process records, review notes and version histories serve as learning evidence. The pathway does not report implementation samples, learning outcomes, patient data, model performance or clinical validation; it is intended only to inform course design and cautious future implementation.

Keywords: Industry-Education Integration; COPD Health Management; Project-Based Learning Pathways; Medical Informatics and Artificial Intelligence; Artificial Intelligence in Medical Education

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