Improving Accuracy of Electronic Notes Using A Faster, Simpler Approach
Improving Accuracy of Electronic Notes Using A Faster, Simpler Approach
批准号:
8805997
负责人:
THOMAS H. PAYNE
金额:
$15.43万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-09-30 至 2016-09-29
中文摘要
描述(由申请人提供):医生进度记录包含对患者护理至关重要的信息,包括病史和体检结果、测试解释、评估和治疗计划。然而,在从纸质医生笔记向电子医生笔记过渡的过程中,许多医生花费了更多的时间来创建它们,这导致了使用复制/粘贴和模板等节省时间的措施,这些措施降低了笔记的准确性和质量。这不仅威胁到笔记最重要的用途--病人护理--而且也威胁到笔记在研究、质量改进和支持报销方面的作用。为了解决这些问题,我们提出了一个具有以下具体目标的项目:1.完善和实现一种新的语音生成增强型电子笔记系统(VGEENS),该系统将语音识别与自然语言处理相结合,并与电子病历(EMR)链接,以提高笔记的准确性和及时性。2.使用随机试验对VGEENS进行评估,每组有30名内科医生参加,以评估电子笔记的准确性、质量、及时性和用户满意度。干预医生将使用VGEENS,而对照医生将像往常一样继续创建笔记。这种新的方法有可能提高记录的准确性,同时减少向其他临床医生提供急诊检查进展记录的延迟。它利用迅速改进的语音识别和NLP技术,使医生能够使用一种自然、快速的方法--人声--将他们的观察和想法传达到EMR记录中。
英文摘要
DESCRIPTION (provided by applicant): Physician progress notes contain information essential to patient care, including findings from history and physical exam, interpretation of tests, assessment and treatment plans. However in the transition from paper to electronic physician notes, many physicians spend more time creating them, which has led to the use of time-saving measures such as copy/paste and templates that have degraded note accuracy and quality. This threatens the usefulness of notes not only for their most important use-patient care-but also for research, quality improvement, and in supporting reimbursement. To address these problems, we propose a project with the following specific aims: 1. To refine and implement a new voice-generated enhanced electronic note system (VGEENS) integrating voice recognition with natural language processing and links to the electronic medical record (EMR) to improve note accuracy and timeliness. 2. To evaluate VGEENS using a randomized trial with 30 internal medicine physicians in each arm to assess electronic note accuracy, quality, timeliness, and user satisfaction. Intervention physicians will use VGEENS, while the control physicians will continue with note creation as they normally would. This novel approach has the potential to improve note accuracy while reducing delays in making progress notes in EMRs available to other clinicians. It leverages rapidly improving voice recognition and NLP technologies to permit physicians to use a natural, fast method-human voice-to convey their observation and thoughts into the EMR record.
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