SBIR Phase I: Contextual ASR to Support EHR Adoption

这个小企业创新研究SBIR第一阶段项目将使用历史医疗记录的统计分析，为传统医疗记录的每个部分创建语言模型家族，并根据叙事笔记中的上下文位置实时切换自动语音识别（ASR）中的词汇。当前的语音识别方法使用单一的通用医学词典来训练识别器识别单词。医疗情境特定的概率被忽略。由于DocTalk引擎将实时集成ASR与自然语言处理（NLP）结合在一起，因此有机会利用NLP上下文数据来实际改变正在进行的ASR过程。这种创新的文本结构方法将利用医疗记录中每个部分使用的语言的统计变异性。这是一个解决工作流程延迟的独特机会；通过使用基于云的开源杠杆解决方案，实现全国性电子医疗保健基础设施的最大障碍。该项目的更广泛的影响/商业潜力包括医生增加可用信息的能力，避免第三方转录错误，减轻工作流程延迟。电子医疗记录（EMR）中的大部分工作流程延迟是由于需要执行手动操作来填写记录中的结构化表单，而不是传统书面笔记和转录中使用的简单非结构化叙述。成功完成这个创新的nlp增强的基于上下文的ASR项目，将为小型初级保健医生提供部署集成的、交互式的、直观的、低成本的数据输入系统所需的准确性，并帮助克服国家电子医疗保健基础设施的最大障碍。

This Small Business Innovative Research SBIR Phase I project will use statistical analysis of historical medical records to create families of language models for each section of the traditional medical note and switch lexicons in and out of the automatic speech recognition (ASR) in real time based on the contextual position within the narrative note. Current speech recognition methods use a single, general-purpose medical lexicon to train a recognizer when identifying words. Medical context-specific probabilities are ignored. Because the DocTalk engine incorporates real time integrated ASR with natural language processing (NLP), there is an opportunity to utilize NLP contextual data to actually change the ongoing ASR process. This innovative text structuring method will exploit the statistical variability of language used in each section of the medical record. It is a unique opportunity to address delay in workflow; the largest barrier to a national electronic healthcare infrastructure, by using a cloud-based, open source leveraged solution.The broader impact/commercial potential of this project includes the ability of physicians to increase usable information, avoid third party transcription errors, and mitigate workflow delays. The majority of workflow delay in electronic medical records (EMR) is the need to perform manual operations to fill structured forms within the record, as opposed to simple unstructured narratives used in traditional written notes and transcriptions. Successful completion of this innovative proposed program of NLP-enhanced context based ASR will provide the accuracy required to deploy an integrated, interactive, intuitive, low-cost data entry system for small practice primary care physicians, and help overcome the largest obstacle to a national electronic healthcare infrastructure.