语音应答分析代理(VERAA):利用大型语言模型绘制 SDoH 调查中的语音应答。

Rishivardhan Krishnamoorthy, Vishal Nagarajan, Hayden Pour, Supreeth P Shashikumar, Aaron Boussina, Emilia Farcas, Shamim Nemati, Christopher S Josef
{"title":"语音应答分析代理(VERAA):利用大型语言模型绘制 SDoH 调查中的语音应答。","authors":"Rishivardhan Krishnamoorthy, Vishal Nagarajan, Hayden Pour, Supreeth P Shashikumar, Aaron Boussina, Emilia Farcas, Shamim Nemati, Christopher S Josef","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments. The agent asks questions in the patient's native language, translates responses into English, and subsequently maps these responses via a large language model (LLM) to structured options in a SDoH survey. This tool can be extended to a variety of survey instruments in either hospital or home settings, enabling the extraction of structured insights from free-text answers. The proposed approach heralds a shift towards more inclusive and insightful data collection, marking a significant stride in SDoH data enrichment for optimizing health outcome predictions and interventions.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2024 ","pages":"258-265"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141834/pdf/","citationCount":"0","resultStr":"{\"title\":\"Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.\",\"authors\":\"Rishivardhan Krishnamoorthy, Vishal Nagarajan, Hayden Pour, Supreeth P Shashikumar, Aaron Boussina, Emilia Farcas, Shamim Nemati, Christopher S Josef\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments. The agent asks questions in the patient's native language, translates responses into English, and subsequently maps these responses via a large language model (LLM) to structured options in a SDoH survey. This tool can be extended to a variety of survey instruments in either hospital or home settings, enabling the extraction of structured insights from free-text answers. The proposed approach heralds a shift towards more inclusive and insightful data collection, marking a significant stride in SDoH data enrichment for optimizing health outcome predictions and interventions.</p>\",\"PeriodicalId\":72181,\"journal\":{\"name\":\"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science\",\"volume\":\"2024 \",\"pages\":\"258-265\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141834/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

健康的社会决定因素(SDoH)已被证明对健康相关结果有着深远的影响,但在电子健康记录(EHR)中,这些数据的缺失率很高。此外,在美国,英语水平有限也会成为与医疗服务提供者沟通的障碍。在这项研究中,我们设计了一个能够在医疗环境中进行 SDoH 调查的多语言对话代理。该代理用患者的母语提问,将回答翻译成英语,然后通过大型语言模型(LLM)将这些回答映射到 SDoH 调查的结构化选项中。该工具可扩展到医院或家庭环境中的各种调查工具,从而能够从自由文本答案中提取结构化见解。所提出的方法预示着向更具包容性和洞察力的数据收集转变,标志着在丰富 SDoH 数据以优化健康结果预测和干预方面取得了重大进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.

Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments. The agent asks questions in the patient's native language, translates responses into English, and subsequently maps these responses via a large language model (LLM) to structured options in a SDoH survey. This tool can be extended to a variety of survey instruments in either hospital or home settings, enabling the extraction of structured insights from free-text answers. The proposed approach heralds a shift towards more inclusive and insightful data collection, marking a significant stride in SDoH data enrichment for optimizing health outcome predictions and interventions.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信