Applying NLP to Free Text as an EHR Data Capture Method to Improve EHR Usability

描述（由申请人提供）：本提案旨在确保“NLP-独立或混合文档”（一种涉及自然语言处理的EHR数据捕获方法，也可能是标准EHR数据捕获方法）的能力，以通过减少文档时间、提高文档质量和提高临床医生满意度来提高EHR的可用性。需要解决的问题。电子健康记录（“EHR”）的有限可用性和缺乏标准化术语阻碍了EHR的采用和最佳使用，因此阻碍了普遍可互操作和基于证据的可报告医疗保健系统的实现。文档所需的大量时间，临床医生满意度低，文档不完整是EHR的问题。创新目前的研究表明，NLP可以用于EHR数据捕获。ZyDoc正在通过评估NLP-独立-或-混合文档的能力来推进研究的状态，以提高EHR可用性沿着几个标准。长期目标。通过实现互操作性和提高EHR的可用性，通过提高临床医生的满意度，提高文档质量，并减少数据采集时间，MediSapien将鼓励广泛采用EHR和结构化数据的最佳使用。第一阶段总结。该拨款提案的第一个具体目标的目的是确保NLP-独立或混合文档能够提高临床医生的满意度，效率和文档质量，相对于标准的EHR数据采集方法。第二个特定目标的目的是提高MediSapien编码的准确性。这些特定目标将确保NLP-独立或混合文档和MediSapien的技术可行性，以提高EHR的可用性。第二阶段目标。在第二阶段，ZyDoc将完成产品开发，在两家医院进行MediSapien的beta测试，并测量产品对临床结果或文档结果的影响。商业机会。ZyDoc将通过与联合收割机MediSapien结合在自己的解决方案中的供应商合作，将MediSapien作为一个模块化组件提供，使他们的客户能够满足EHR有意义的使用标准。 公共卫生相关性：电子健康记录（“EHR”）的有限可用性和缺乏标准化术语阻碍了EHR的采用和有意义的使用，因此阻碍了普遍可互操作和基于证据的可报告医疗保健系统的实现。该提案旨在证明，通过应用NLP和其他技术将口述和转录的非结构化文本转换为结构化数据并将其插入到EHR中，可以提高EHR的可用性。实现这一结果将鼓励最佳的EHR使用可搜索的结构化数据，从而实现互操作性。

DESCRIPTION (provided by applicant): This proposal aims to ensure the ability of "NLP-Standalone-or-Hybrid Documentation," a method of EHR data capture involving Natural Language Processing and possibly also standard EHR data capture, to improve the usability of EHR by reducing documentation time, increasing documentation quality, and increasing clinician satisfaction. Problem to be Addressed. Limited usability of the Electronic Health Record ("EHR") and lack of standardized terminology impedes EHR adoption and optimal use, and therefore hinders realization of a universally interoperable and evidence-based reportable health care system. Large amounts of time required for documentation, low clinician satisfaction, and incomplete documentation are problems plaguing EHR. Innovation. Current research has demonstrated that NLP may be used for EHR data capture. ZyDoc is furthering the state of research by assessing the capability of NLP-Standalone-or-Hybrid Documentation to improve EHR usability along several criteria. Long Term Goal. By enabling interoperability and improving EHR usability, through improving clinician satisfaction, improving documentation quality, and reducing data capture time, MediSapien will encourage widespread EHR adoption and optimal use with structured data. Phase I Summary. The purpose of the first Specific Aim of this grant proposal is to ensure that NLP- Standalone-or-Hybrid Documentation is capable of improving clinician satisfaction, efficiency, and documentation quality, relative to standard EHR data capture methods. The purpose of the second Specific Aim is to improve the accuracy of MediSapien's coding. These Specific Aims will ensure the technical feasibility of NLP-Standalone-or-Hybrid Documentation and MediSapien for improving EHR usability. Phase II Objectives. In Phase II, ZyDoc will complete product development, beta test MediSapien at two hospitals, and measure the product's impact on clinical outcomes or documentation results. Commercial Opportunity. ZyDoc will offer MediSapien as a modular component by partnering with vendors that combine MediSapien in their own solutions, enabling their clients to meet EHR meaningful use standards. PUBLIC HEALTH RELEVANCE: Limited usability of the Electronic Health Record ("EHR") and lack of standardized terminology impedes EHR adoption and meaningful use, and therefore hinders realization of a universally interoperable and evidence- based reportable health care system. This proposal aims to prove that EHR usability can be increased by applying NLP and other technologies to convert dictated and transcribed unstructured text to structured data and inserting it into the EHR. Achievement of this result will encourage optimal EHR use with searchable, structured data that will enable interoperability.