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

描述(由申请人提供)：本建议旨在确保“NLP-独立或混合文档”的能力，这是一种涉及自然语言处理的电子病历数据捕获方法，可能还包括标准的电子病历数据捕获，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改善电子病历的可用性。需要解决的问题。电子健康记录(“EHR”)的使用有限和缺乏标准化术语阻碍了EHR的采用和最佳使用，从而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。记录所需的大量时间、临床医生满意度低以及记录不完整是困扰EHR的问题。创新。目前的研究表明，NLP可用于EHR数据捕获。ZyDoc正在通过评估NLP-独立或混合文档的能力来按照几个标准改进EHR的可用性，从而进一步推动研究状态。长期目标。MediSapien将通过提高临床医生满意度、提高文档质量和缩短数据捕获时间来实现互操作性和改善电子病历的可用性，从而鼓励广泛采用电子病历并优化结构化数据的使用。第一阶段总结。这项赠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数据捕获方法。第二个具体目的是提高MediSapien编码的准确性。这些具体目标将确保NLP-独立或混合文档和MediSapien在提高EHR可用性方面的技术可行性。第二阶段目标。在第二阶段，ZyDoc将完成产品开发，在两家医院对MediSapien进行Beta测试，并衡量该产品对临床结果或文档结果的影响。商机。ZyDoc将通过与供应商合作，将MediSapien作为模块化组件提供，这些供应商将MediSapien结合到他们自己的解决方案中，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”)的可用性有限，缺乏标准化的术语，阻碍了电子健康记录的采用和有意义的使用，从而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。这项提议旨在证明，通过应用NLP和其他技术将听写和转录的非结构化文本转换为结构化数据并将其插入到电子病历中，可以提高电子病历的可用性。这一结果的实现将鼓励优化电子病历与可搜索的结构化数据的使用，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.