用AI模型自动评估护理操作，提升培训效率与一致性
Automated Procedural Analysis via Video-Language Models for AI-assisted Nursing Skills Assessment
- 基于视频语言模型，分步识别护理动作并定位错误
- 可准确检测缺失或错误步骤，支持时间定位与解释
- 适合护理教育机构和智能培训系统使用
高质量的护理服务对患者安全至关重要，但当前护理教育依赖主观且耗时的教师反馈，限制了培训的可扩展性和效率，进而影响护生入职时的胜任力。本文提出一种基于视频语言模型（VLM）的框架，实现护理技能训练的自动化流程评估与反馈，具备集成到现有培训体系的潜力。该框架借鉴人类技能习得路径，按课程式推进：从高层动作识别，到细粒度子动作分解，最终实现流程推理。此设计在降低教师负担的同时保持评估质量，提供三大核心能力：1）通过识别护理教学视频中缺失或错误的子动作来诊断错误；2）生成可解释的反馈，说明某步骤为何顺序错误或被遗漏；3）实现客观、一致的形成性评价。在合成视频上的验证表明，系统具备可靠的错误检测与时间定位能力，证实其应对真实训练变异性的潜力。该工作解决了流程瓶颈，推动了大规模、标准化评估，助力护理教育智能化，最终提升医护队伍能力与患者安全。
原文摘要 · Abstract (English)
Consistent high-quality nursing care is essential for patient safety, yet current nursing education depends on subjective, time-intensive instructor feedback in training future nurses, which limits scalability and efficiency in their training, and thus hampers nursing competency when they enter the workforce. In this paper, we introduce a video-language model (VLM) based framework to develop the AI capability of automated procedural assessment and feedback for nursing skills training, with the potential of being integrated into existing training programs. Mimicking human skill acquisition, the framework follows a curriculum-inspired progression, advancing from high-level action recognition, fine-grained subaction decomposition, and ultimately to procedural reasoning. This design supports scalable evaluation by reducing instructor workload while preserving assessment quality. The system provides three core capabilities: 1) diagnosing errors by identifying missing or incorrect subactions in nursing skill instruction videos, 2) generating explainable feedback by clarifying why a step is out of order or omitted, and 3) enabling objective, consistent formative evaluation of procedures. Validation on synthesized videos demonstrates reliable error detection and temporal localization, confirming its potential to handle real-world training variability. By addressing workflow bottlenecks and supporting large-scale, standardized evaluation, this work advances AI applications in nursing education, contributing to stronger workforce development and ultimately safer patient care.
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