DeepSeek赋能的儿科全流程智慧医疗系统的构建和应用效果评价OA北大核心
A DeepSeek-enabled intelligent pediatric healthcare system:Construction and application effectiveness evaluation
背景 复旦大学附属儿科医院基于前期自行研发的智能辅助诊断工具,融合本地化部署的DeepSeek大模型,构建智慧医疗系统,以提升儿科医疗服务效率和医患满意度.目的 评价已构建的智慧医疗系统在儿科医院患儿全流程医疗服务真实场景中的应用效果.设计 横断面调查.方法 结合医学知识库、知识图谱及检索增强生成等技术,在原有"小布AI医生"基础上,构建覆盖诊前、诊中、诊后全流程的儿科智慧医疗系统(简称DS-小布医生2.0),通过医院大数据平台采集性能指标,并分别以随机数字表法抽取50名门诊医生、以便利抽样法选取200名患儿家长,进行可用性评估.主要结局指标 系统的总体性能及诊前、诊中和诊后评价指标.结果 DS-小布医生2.0于2025年3月3日至5月11日临床应用期间,系统服务11 957人次患儿,累计使用86 533次.核心性能表现为:峰值CPU利用率5%,推理链路完成时间5.9 s,医学推理准确率81.5%.全流程各阶段指标优异:诊前导诊建议使用率82.3%;诊中诊断准确率92.4%,信息提取准确率96.4%;诊后患儿随访依从率74.0%.该系统的准确率为92.4%,双语评估替补(4元语法)指标(BLEU-4)评分为0.87,面向召回的摘要评估指标(最长公共子序列)(ROUGE-L)评分为0.73,交叉熵指标(CEM)为0.92,以上指标均优于美国OPEN AI公司的GPT-4 Med模型和来自美国斯坦福大学的Bio MedLM模型.在用户可用性评价方面,50名医生完成测试后系统可用性问卷(PSSUQ)调查,在系统质量、信息质量、界面质量和总体评价方面可用性较好;采用净推荐值量表(NPS)对200名患儿家长进行调查,净推荐值达+78分.结论 DS-小布医生2.0实现了高精度的儿科全流程智慧医疗服务,用户满意度高,为缓解儿科医疗资源短缺问题提供了有效的技术方案.
Background The Children's Hospital of Fudan University developed an intelligent diagnostic assistance system by integrating its self-developed diagnostic tool with locally deployed DeepSeek large language models to enhance pediatric medical service efficiency and physician-patient satisfaction.Objective To evaluate the application effectiveness of the constructed intelligent healthcare system based on DeepSeek large language models in real-world pediatric patient care scenarios across the complete medical service workflow.Design Cross-sectional survey.Methods Integrating medical knowledge bases,knowledge graphs,and retrieval-augmented generation technologies,the team upgraded the original"Xiaobu AI Doctor,"creating a comprehensive intelligent medical system covering pre-consultation,consultation,and post-consultation workflows.After clinically implementation,performance metrics were automatically collected via the hospital's big data platform.Usability was evaluated through surveys of 50 randomly selected outpatient physicians and 200 patients' families.Main outcome measures Overall system performance and evaluation metrics for the pre-consultation,consultation,and post-consultation phases.Results During clinical implementation(March 3 to May 11,2025),the system served 11,957 pediatric patient visits with 86,533 cumulative interactions.Core performance showed 5%peak CPU utilization,5.9-second inference latency,and 81.5%medical reasoning accuracy.Phase-specific results included:82.3%triage recommendation utilization(pre-consultation);92.4%diagnostic accuracy and 96.4%information extraction accuracy(consultation);74.0%follow-up compliance(post-consultation).In comparative evaluation,DS-Xiaobu Doctor 2.0 outperformed GPT-4 Med(OpenAI)and BioMedLM(Stanford University)across key metrics:accuracy(92.4%),BLEU-4(0.87),ROUGE-L(0.73),and CEM(0.92).Physicians(n=50)reported high usability in system quality,information quality,interface quality,and overall satisfaction via the Post-Study System Usability Questionnaire(PSSUQ).Patient families(n=200)gave a Net Promoter Score(NPS)of+78.Conclusion DS-Xiaobu Doctor 2.0 delivered high-precision intelligent pediatric medical services across the complete care workflow with high user satisfaction,providing an effective technological solution for addressing shortages in pediatric medical resources.
张晓波;祁媛媛;张玉蓉;安海龙;王艺;李倩;冯瑞;杨睿;叶成杰;王新;葛小玲;史雨;王立波;傅唯佳
复旦大学附属儿科医院 上海,201102复旦大学附属儿科医院 上海,201102复旦大学附属儿科医院 上海,201102复旦大学计算与智能创新学院 上海,200433复旦大学附属儿科医院 上海,201102复旦大学附属儿科医院 上海,201102复旦大学计算与智能创新学院 上海,200433复旦大学附属儿科医院 上海,201102复旦大学附属儿科医院 上海,201102复旦大学计算与智能创新学院 上海,200433复旦大学附属儿科医院 上海,201102复旦大学附属儿科医院 上海,201102复旦大学附属儿科医院 上海,201102复旦大学附属儿科医院 上海,201102
DeepSeek医学大语言模型智慧医疗儿科
DeepSeekMedical large language modelIntelligent healthcarePediatric
《中国循证儿科杂志》 2025 (3)
217-222,6
上海申康发展中心市级医院新兴前沿联合攻关项目:SHDC12024136上海市促进产业高质量发展专项资金产业战略关键领域技术攻关人工智能专题项目:2024-GZL-RGZN-01013复旦大学附属儿科医院"医+X"交叉创新团队孵化项目:EKYX202409
评论