Breaking bad news with reflective AI virtual experience (BRAVE): development and evaluation of a generative AI-based simulation tool.

IF 3.6 Q2 HEALTH CARE SCIENCES & SERVICES
Orit Karnieli-Miller, Zohar Elyoseph
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引用次数: 0

Abstract

BACKGROUND: Breaking bad news (BBN) to patients is complex and emotionally and cognitively demanding. The way bad news is delivered has implications for patients, their significant others, and healthcare professionals. Despite the development of structured BBN protocols, many health professionals report insufficient training and low self-efficacy for conducting these conversations. While best-practice BBN education exist, opportunities for repeated practice with high-quality feedback are limited, representing a critical barrier to scalability and global health equity, particularly in resource-constrained settings. We developed BRAVE (Breaking Bad News with AI Reflective Virtual Experience), a generative AI-based simulator that includes a written BBN encounter with a virtual patient. Unlike prior AI simulators that primarily offered structured summary feedback, BRAVE explicitly combines a prompted, dialogic reflective debrief followed by personalized feedback mapped to the SPwICES protocol. METHODS: A prospective, single arm, pre-post design, which incorporates both quantitative and qualitative measures. Ninety-nine senior medical students completed one BRAVE session following a required BBN course. Pre/post questionnaires assessed self-efficacy, willingness to engage in BBN, and intention to apply the BBN protocol. User experience was rated on a 5-point scale. Quantitative analysis included paired-sample t-tests and descriptive statistics. Qualitative data from open-ended responses were analyzed using immersion/crystallization. RESULTS: Self-efficacy increased from M = 6.95 to M = 7.62 (p<.001), willingness from M = 5.80 to M = 6.51 (p<.001), and intention to apply the protocol from M = 8.20 to M = 8.41 (p=.005). User experience was positive: interaction quality (M = 4.27), structured feedback (M = 4.19), reflective dialogue (M = 3.91), and interest in future use (M = 3.81). Qualitative analysis showed most students described the simulator as realistic, useful, and safe for practice, emphasizing opportunities to think, reread, and make mistakes with reduced embarrassment; a minority noted limits of the written modality (restricted nonverbal cues and occasional recognition errors), which were addressed during refinement. CONCLUSIONS: A brief text-based GenAI simulation integrating dialogic reflection with protocol-based feedback yielded improvements in learners’ confidence and willingness to conduct BBN and was well-received. BRAVE can complement existing curricula by offering scalable, psychologically safe, and feedback-rich practice; future controlled studies with performance-based outcomes are warranted.

反思性人工智能虚拟体验(BRAVE)的坏消息:基于生成人工智能的仿真工具的开发和评估。
背景:突发坏消息(BBN)对患者来说是复杂的,在情感和认知上都有要求。坏消息传递的方式对患者、他们的另一半和医疗保健专业人员都有影响。尽管发展了结构化的BBN协议,但许多卫生专业人员报告说,进行这些对话的培训不足,自我效能低。虽然存在最佳实践BBN教育,但获得高质量反馈的重复实践机会有限,这是可扩展性和全球卫生公平的一个重大障碍,特别是在资源有限的环境中。我们开发了BRAVE (Breaking Bad News with AI Reflective Virtual Experience),这是一个基于人工智能的生成模拟器,包括与虚拟病人的书面BBN相遇。与之前主要提供结构化总结反馈的人工智能模拟器不同,BRAVE明确地结合了提示的、对话式的反思汇报,然后是映射到SPwICES协议的个性化反馈。方法:前瞻性,单臂,前后设计,结合定量和定性措施。99名医学院高年级学生在必修的BBN课程后完成了一次BRAVE课程。前/后问卷评估自我效能、参与BBN的意愿和应用BBN方案的意愿。用户体验评分为5分。定量分析包括配对样本t检验和描述性统计。使用浸泡/结晶法分析开放式回答的定性数据。结果:自我效能感从M = 6.95提高到M = 7.62 (p = 0.05)
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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来源期刊
CiteScore
5.70
自引率
0.00%
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审稿时长
12 weeks
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