Contextual ASR to Support EHR Adoption

描述(申请人提供)：采用电子健康记录(EHR)系统是国家医疗保健优先事项。然而，研究表明，过渡到EHR后，医生的生产力会大幅下降25%-40%。大部分工作流程延误是因为需要执行手动操作来填写电子病历中的结构化表格，而不是传统书面笔记和抄本中使用的简单的非结构化叙述。先锋医疗技术公司(VMT)获得美国国立卫生研究院1R43LM010750拨款，证明了DocTalk的可行性，DocTalk是一种实时、语音驱动、开源的增强型小型实践遭遇记录系统，利用云中集成的自动语音识别(ASR)和自然语言处理(NLP)，将语音到文本再到结构化医疗数据再到EHR输入。虽然NLP在第一阶段的准确率很高，但在医生复查之前，声音的准确性是不够的。幸运的是，ASR和NLP的紧密集成以及医生笔记的正式结构提供了独特的基于背景的方法来应对这一挑战。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。特定于医学背景的概率被忽略。这个第一阶段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会面笔记，为患者会面笔记的每个部分创建文本语料库2.使用行业标准的开源统计语言建模工具，建立专门用于识别与患者会面笔记的每个特定部分有关的语音的部门特定统计语言模型(SS-SLM)家族。3.使用NLP技术从每个章节的文本中推断语言使用模式，a.检测要用作调用SS-SLM的触发词的章节边界b.确定每个章节的特征词分布4.评估由于使用SS-SLM而提高的每个章节的准确度，目标是与相同医学口述系统中的非特定章节SLM相比，总体减少50%的错误。 公共卫生相关性：NLP增强型基于情景的ASR这一创新建议项目的成功完成，将提供为小型执业初级保健医生部署集成、交互、直观、低成本的数据录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的信息，避免第三方转录错误，并减少工作流程延迟。增加小型实践电子病历的采用直接满足国家医疗保健目标。

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.