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

• 批准号: 7691691
• 负责人: PETER SZOLOVITS
• 金额: $ 42.91万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-30 至 2011-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7691691
• 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

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、准确地收集临床数据是一个长期目标，无论是手动记录保存还是通过电子记录系统都无法实现。该拟议项目从临床过程的一开始就解决了这个问题，旨在改善患者和提供者之间面对面互动期间相关医疗事实的捕获。所提出的系统将“聆听”对话，使用自动语音识别来生成所说内容的（不完美）记录，并应用各种文本分析和分析，而不是依赖提供者的错误记忆来记录访问后的事实。提取方法来创建遭遇的草稿记录。此外，它将提供一个界面，允许患者和提供者检查记录的事实并纠正和完善它们，也使用语音作为主要界面。 该项目的目的是开发和集成实现这一目标所需的组件，与儿童医院环境健康诊所的研究人员合作创建一个测试平台，其中实验可以指导系统开发和评估进展，并进行一系列评估一系列目标的评估。首先，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，它将评估该系统可以收集比常规收集的记录更完整、更准确的记录的假设。随后，它将评估临床医生使用该系统所花费的时间、该系统对患者与提供者接触的干扰程度、患者使用该系统对其记录进行添加和更正的能力，以及患者和提供者对系统使用的主观反应。 这项工作的成功应该会带来基于更完整和准确数据的更好的临床护理。此外，临床数据也正在成为进行转化医学研究的重要资源，其中改进的数据显然非常有价值。