Contextual ASR to Support EHR Adoption

支持 EHR 采用的情境 ASR

• 批准号: 8253003
• 负责人: Daniel Jay Riskin
• 金额: $ 15万
• 依托单位: HEALTH FIDELITY, INC.
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-10 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8253003
• 关键词: Address; Adoption; Characteristics; Code; Data; Documentation; Drops; Electronic Health Record; Electronics; Family; Genetic Transcription; Goals; Grant; Healthcare; Industry; Language; Libraries; Manuals; Medical; Medical Records; Medical Technology; Methods; Modeling; Natural Language Processing; Patients; Pattern; Phase; Physicians; Positioning Attribute; Primary Care Physician; Probability; Process; Productivity; Research Infrastructure; Safety; Small Business Innovation Research Grant; Solutions; Speech; Stream; Structure; System; Techniques; Testing; Text; Time; Training; United States National Institutes of Health; Variant; Voice; Writing; base; cost; innovation; open source; operation; programs; speech recognition; tool; voice recognition

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.

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，过渡到EHR后，大量医师生产率下降了25-40％。大多数工作流程延迟是基于需要执行手动操作以填充EHR内的结构化表格的需求，而不是传统的书面笔记和抄录中使用的简单的非结构化叙述。 Vanguard医疗技术（VMT），根据NIH授予的1R43LM010750，证明是可行性的，这是一种实时，语音驱动的，言语驱动，开放式，开放式，小型实践相遇记录系统，该系统将语音与结构化的医学数据相结合到EHR输入，利用集成的自动化语音识别（ASR）和自然语言（ASR）和NLP（NLP）。虽然第一阶段的NLP准确性很高，但在医师审查之前的语音精度不足。幸运的是，ASR和NLP的紧密整合与医师笔记的形式结构相结合，提供了基于上下文的独特方法来应对挑战。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。特定于医学上下文的概率被忽略。该第I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事结构遭遇注释2。为患者遭遇注释的每个部分创建一个文本语料库。 3.使用NLP技术从每个部分的文本中推断出语言使用模式。要检测截面边界，用作调用SS-SLMS b的触发单词b。为了确定每个第4节的特征性单词分布。根据使用SS-SLMS的使用，评估每节的准确性提高，与同一医学义务系统中的非部分特异性SLM相比，错误的目标是50％的误差降低。 公共卫生相关性：成功完成基于NLP增强上下文ASR的创新拟议计划，将为小型初级保健医生提供部署集成，互动，直观，低成本数据输入系统所需的准确性。增强的医生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轻工作流程延迟。小型实践EHR采用直接解决了国家医疗保健目标。