评估ChatGPT在门诊分诊指导中的应用：一项比较研究
Evaluating the Application of ChatGPT in Outpatient Triage Guidance: A Comparative Study
AI总结：
本研究比较了ChatGPT-3.5与4.0在门诊分诊指导中的回答一致性，发现4.0版本内一致性更高但3.5回答更完整，版本间一致性较低，为LLMs在门诊场景的应用提供了实证依据。
摘要：
人工智能（AI）在医疗保健中的整合为提升运营效率和健康结果带来了变革性潜力。大型语言模型（LLMs），如ChatGPT，已展现出支持医疗决策的能力。将LLMs嵌入医疗系统正成为医疗保健发展中一个有前景的趋势。ChatGPT解决急诊科分诊问题的潜力已得到检验，但很少有研究探索其在门诊部门的应用。本研究以简化工作流程和提升门诊分诊效率为重点，具体旨在评估ChatGPT在门诊指导中所提供回答的一致性，包括版本内回答分析和版本间比较。在版本内方面，结果表明ChatGPT-4.0的内部回答一致性显著高于ChatGPT-3.5（p=0.03），且两者在其首要推荐上均具有中等一致性（4.0为71.2%，3.5为59.6%）。然而，版本间一致性相对较低（平均一致性得分=1.43/3，中位数=1），表明两个版本之间匹配的推荐很少。此外，在比较中仅有50%的首要推荐完全匹配。有趣的是，ChatGPT-3.5的回答比ChatGPT-4.0的回答更可能完整（p=0.02），这表明两个版本在信息处理和回答生成方面可能存在差异。研究结果为AI辅助门诊运营提供了见解，同时也促进了对LLMs在医疗保健应用中潜力和局限性的探索。未来研究可侧重于基于人机工程学和人为因素原则谨慎优化LLMs及医疗系统中的AI整合，以精确契合有效门诊分诊的具体需求。
英文摘要：
The integration of Artificial Intelligence (AI) in healthcare presents a transformative potential for enhancing operational efficiency and health outcomes. Large Language Models (LLMs), such as ChatGPT, have shown their capabilities in supporting medical decision-making. Embedding LLMs in medical systems is becoming a promising trend in healthcare development. The potential of ChatGPT to address the triage problem in emergency departments has been examined, while few studies have explored its application in outpatient departments. With a focus on streamlining workflows and enhancing efficiency for outpatient triage, this study specifically aims to evaluate the consistency of responses provided by ChatGPT in outpatient guidance, including both within-version response analysis and between-version comparisons. For within-version, the results indicate that the internal response consistency for ChatGPT-4.0 is significantly higher than ChatGPT-3.5 (p=0.03) and both have a moderate consistency (71.2% for 4.0 and 59.6% for 3.5) in their top recommendation. However, the between-version consistency is relatively low (mean consistency score=1.43/3, median=1), indicating few recommendations match between the two versions. Also, only 50% top recommendations match perfectly in the comparisons. Interestingly, ChatGPT-3.5 responses are more likely to be complete than those from ChatGPT-4.0 (p=0.02), suggesting possible differences in information processing and response generation between the two versions. The findings offer insights into AI-assisted outpatient operations, while also facilitating the exploration of potentials and limitations of LLMs in healthcare utilization. Future research may focus on carefully optimizing LLMs and AI integration in healthcare systems based on ergonomic and human factors principles, precisely aligning with the specific needs of effective outpatient triage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
