Contextual ASR to Support EHR Adoption

描述（由申请人提供）：采用电子健康记录（EHR）系统是国家医疗保健的优先事项。然而，研究表明，在过渡到EHR后，医生的生产力下降了25-40%。大多数工作流程延迟是基于需要执行手动操作以填写EHR中的结构化表格，而不是传统书面笔记和电子病历中使用的简单非结构化叙述。Vanguard Medical Technologies（VMT）在NIH资助1 R43 LM 010750下证明了DocTalk的可行性，DocTalk是一种实时，语音驱动，开源增强的小型实践遇到记录系统，可将语音处理为文本，将结构化医疗数据处理为EHR输入，利用集成的自动语音识别（ASR）和云中的自然语言处理（NLP）。虽然第一阶段的NLP准确性很高，但医生审查之前的语音准确性不足。幸运的是，ASR和NLP的紧密集成与医生笔记的正式结构相结合，提供了独特的基于上下文的方法来应对挑战。当前的语音识别方法在识别单词时使用单个通用医学词典来训练识别器。忽略医疗背景特定的概率。第一阶段SBIR项目的四个具体目标是：1。通过处理100万个基于文本的叙述性结构化就诊笔记，为患者就诊笔记的每个部分创建文本语料库2。使用行业标准开源统计语言建模工具，构建一系列特定章节统计语言模型（SS-SLM），专门用于识别与患者就诊记录的每个特定章节相关的语音。3.使用NLP技术从每个部分的文本中推断语言使用模式。检测要用作用于调用SS-SLM的触发字的区段边界B。确定每个部分的特征词分布4.评估由于使用SS-SLM而导致的每节准确性的提高，目标是在同一医疗听写系统中，与非节特定SLM相比，总体错误减少50%。 公共卫生相关性：成功完成这一创新的NLP增强的基于上下文的ASR计划，将为小型实践初级保健医生提供部署集成，交互，直观，低成本数据输入系统所需的准确性。增强的DocTalk系统将使医生能够增加可用信息，避免第三方转录错误，并减少工作流程延迟。越来越多的小型实践EHR采用直接解决了国家医疗保健目标。

DESCRIPTION (provided by applicant): The adoption of electronic health record (EHR) systems is a national healthcare priority. However studies show massive physician productivity drop of up to 25-40% upon transition to EHR. The majority of workflow delay is based on the need to perform manual operations to fill structured forms within the EHR, as opposed to simple unstructured narratives used in traditional written notes and transcriptions. Vanguard Medical Technologies (VMT), under NIH grant 1R43LM010750, proved feasibility for DocTalk, a real-time, speech-driven, open-source augmented, small practice encounter recording system that processes voice to text to structured medical data to EHR input, utilizing integrated automated speech recognition (ASR) and natural language processing (NLP) in the cloud. While NLP accuracy in Phase I was high, voice accuracy prior to physician review was inadequate. Fortunately, the tight integration of ASR and NLP combined with the formal structure of physician notes offers unique context based approaches to address the challenge. Current speech recognition methods use a single general-purpose medical lexicon to train a recognizer when identifying words. Medical context-specific probabilities are ignored. The four Specific Aims of this Phase I SBIR project are to: 1. Create a textual corpus for each section of a patient encounter note by processing 1 million text based narrative structured encounter notes 2. Build a family of Section-Specific Statistical Language Models (SS-SLMs) specialized in recognizing speech pertaining to each specific section of a patient encounter note, using industry standard open source statistical language modeling tools. 3. Use NLP techniques to infer patterns of language usage from text of each section, a. To detect section boundaries to be used as trigger words for invoking SS-SLMs b. To determine characteristic word distributions of each section 4. Assess improvement in accuracy per section due to use of SS-SLMs, with the goal of 50% overall reduction of errors compared to non-section-specific SLMs in the same medical dictation system. PUBLIC HEALTH RELEVANCE: 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. The augmented DocTalk system will enable physicians to increase usable information, avoid third-party transcription errors, and mitigate workflow delays. Increased small practice EHR adoption directly addresses national healthcare goals.