Capturing Patient-Provider Encounter through Text Speech and Dialogue Processing

通过文本语音和对话处理捕捉患者与提供者的相遇

基本信息

批准号：
7934457
负责人：
PETER SZOLOVITS
金额：
$ 42.48万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-30 至 2012-09-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7934457
关键词：
Address Adopted Arts Beds Boston Childhood Clinic Clinical Clinical Data Collaborations Collection Communication Computer Systems Computer Systems Development Computer software Computers Conflict (Psychology)Data Diagnosis Discipline of Nursing Disease Documentation Environment Environmental Health Evaluation Feedback Genetic Transcription Goals Health Personnel Healthcare Individual Lead Linguistics Manuals Medical Medical Records Memory Methods Modeling Paper Patients Pediatric Hospitals Pharmaceutical Preparations Privacy Process Provider Records Research Research Ethics Committees Research Personnel Resources Running Series Signs and Symptoms Speech Stream Structure System Techniques Technology Test Result Testing Text Time Transcript Visit Visual Work base clinical care clinically significant design electronic recording system experience improved information organization patient safety prototype research study response speech recognition success translational medicine

项目摘要

DESCRIPTION (provided by applicant): Complete and accurate collection of clinical data in the course of health care is a long-standing goal that has not been achieved either by manual record-keeping or through electronic record systems. This proposed project addresses the problem from the beginning of the clinical process, by aiming to improve the capture of relevant medical facts during the face-to-face interaction between a patient and provider. Instead of relying on the provider's fallible memory to record facts after the visit, the proposed system will "listen" to the conversation, use automatic speech recognition to produce an (imperfect) record of what was said, and apply a variety of text analysis and extraction methods to create a draft record of the encounter. Further, it will provide an interface that should permit patients and providers to examine the facts that were recorded and to correct and complete them, also using speech as the primary interface. The projects aims are to develop and integrate the components needed to accomplish this goal, to create a testbed in collaboration with researchers at the environmental health clinic of a children's hospital in which experiments can guide system development and assess progress, and to conduct a series of evaluations that assess a series of objectives. First, the research will characterize the ability of the speech recognition, information extraction and information organization components to process the target conversations. Second, it will evaluate the hypothesis that this system can collect a more complete and accurate record than what is routinely collected. Subsequently, it will evaluate the time taken by clinicians to use the system, the extent to which the system is seen to disrupt the patient-provider encounter, the ability of patients to use the system to make additions and corrections to their records, and the subjective response of both patients and providers to use of the system. Success in this effort should lead to better clinical care that is based on more complete and accurate data. In addition, clinical data are also becoming an important resource in the conduct of translational medicine research, where improved data are obviously highly valuable.

描述（由申请人提供）：在医疗保健过程中，完整而准确的临床数据收集是一个长期的目标，既没有通过手动记录或通过电子记录系统来实现的。这项拟议的项目从临床过程开始就解决了问题，目的是改善患者与提供者之间面对面互动期间对相关医疗事实的捕获。拟议的系统将不依靠提供商的易犯错误记忆来记录事实，而是将“聆听”对话，使用自动语音识别来产生（不完美的）记录，并应用各种文本分析和提取方法来创建相遇记录。此外，它将提供一个界面，该界面应允许患者和提供者检查所记录的事实并纠正和完成它们，并使用语音作为主要界面。这些项目的目的是开发和整合实现这一目标所需的组件，以与儿童医院环境卫生诊所的研究人员合作创建一个测试台，实验可以指导系统开发和评估进度，并进行一系列评估一系列目标的评估。首先，该研究将表征语音识别，信息提取和信息组织组件处理目标对话的能力。其次，它将评估以下假设：该系统可以收集比通常收集的系统更完整，更准确的记录。随后，它将评估临床医生使用该系统所花费的时间，该系统被视为破坏患者提供者的遭遇的程度，患者使用系统对记录进行补充和校正的能力以及患者和提供者对使用该系统的主观反应。在这项工作中的成功应该导致基于更完整和准确的数据，这将带来更好的临床护理。此外，临床数据也已成为转化医学研究进行的重要资源，在这些研究中，改进的数据显然是高价的。