Kaufman David R, Sheehan Barbara, Stetson Peter, Bhatt Ashish R, Field Adele I, Patel Chirag, Maisel James Mark
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, United States.
Health Strategy and Solutions, Intel Corp, Santa Clara, CA, United States.
JMIR Med Inform. 2016 Oct 28;4(4):e35. doi: 10.2196/medinform.5544.
The process of documentation in electronic health records (EHRs) is known to be time consuming, inefficient, and cumbersome. The use of dictation coupled with manual transcription has become an increasingly common practice. In recent years, natural language processing (NLP)-enabled data capture has become a viable alternative for data entry. It enables the clinician to maintain control of the process and potentially reduce the documentation burden. The question remains how this NLP-enabled workflow will impact EHR usability and whether it can meet the structured data and other EHR requirements while enhancing the user's experience.
The objective of this study is evaluate the comparative effectiveness of an NLP-enabled data capture method using dictation and data extraction from transcribed documents (NLP Entry) in terms of documentation time, documentation quality, and usability versus standard EHR keyboard-and-mouse data entry.
This formative study investigated the results of using 4 combinations of NLP Entry and Standard Entry methods ("protocols") of EHR data capture. We compared a novel dictation-based protocol using MediSapien NLP (NLP-NLP) for structured data capture against a standard structured data capture protocol (Standard-Standard) as well as 2 novel hybrid protocols (NLP-Standard and Standard-NLP). The 31 participants included neurologists, cardiologists, and nephrologists. Participants generated 4 consultation or admission notes using 4 documentation protocols. We recorded the time on task, documentation quality (using the Physician Documentation Quality Instrument, PDQI-9), and usability of the documentation processes.
A total of 118 notes were documented across the 3 subject areas. The NLP-NLP protocol required a median of 5.2 minutes per cardiology note, 7.3 minutes per nephrology note, and 8.5 minutes per neurology note compared with 16.9, 20.7, and 21.2 minutes, respectively, using the Standard-Standard protocol and 13.8, 21.3, and 18.7 minutes using the Standard-NLP protocol (1 of 2 hybrid methods). Using 8 out of 9 characteristics measured by the PDQI-9 instrument, the NLP-NLP protocol received a median quality score sum of 24.5; the Standard-Standard protocol received a median sum of 29; and the Standard-NLP protocol received a median sum of 29.5. The mean total score of the usability measure was 36.7 when the participants used the NLP-NLP protocol compared with 30.3 when they used the Standard-Standard protocol.
In this study, the feasibility of an approach to EHR data capture involving the application of NLP to transcribed dictation was demonstrated. This novel dictation-based approach has the potential to reduce the time required for documentation and improve usability while maintaining documentation quality. Future research will evaluate the NLP-based EHR data capture approach in a clinical setting. It is reasonable to assert that EHRs will increasingly use NLP-enabled data entry tools such as MediSapien NLP because they hold promise for enhancing the documentation process and end-user experience.
电子健康记录（EHR）中的文档记录过程耗时、低效且繁琐。听写结合人工转录的方式已越来越普遍。近年来，基于自然语言处理（NLP）的数据采集成为一种可行的数据录入替代方法。它使临床医生能够掌控该过程，并有可能减轻文档记录负担。问题依然存在，即这种基于NLP的工作流程将如何影响EHR的可用性，以及它在增强用户体验的同时能否满足结构化数据和其他EHR要求。
本研究的目的是评估一种基于NLP的数据采集方法（使用听写和从转录文档中提取数据（NLP录入））在文档记录时间、文档记录质量和可用性方面与标准EHR键盘和鼠标数据录入相比的相对有效性。
这项形成性研究调查了EHR数据采集的4种NLP录入和标准录入方法（“协议”）组合的使用结果。我们将一种使用MediSapien NLP进行结构化数据采集的基于听写的新协议（NLP - NLP）与标准结构化数据采集协议（标准 - 标准）以及2种新的混合协议（NLP - 标准和标准 - NLP）进行了比较。31名参与者包括神经科医生、心脏病专家和肾病专家。参与者使用4种文档记录协议生成了4份会诊或入院记录。我们记录了任务时间、文档记录质量（使用医生文档质量工具，PDQI - 9）以及文档记录过程的可用性。
在3个学科领域共记录了118份记录。与分别使用标准 - 标准协议的16.9分钟、20.7分钟和21.2分钟以及使用标准 - NLP协议（2种混合方法之一）的13.8分钟、21.3分钟和18.7分钟相比，NLP - NLP协议记录每份心脏病记录的中位时间为5.2分钟，每份肾病记录为7.3分钟，每份神经科记录为8.5分钟。使用PDQI - 9工具测量的9个特征中的8个，NLP - NLP协议的中位质量得分总和为24.5；标准 - 标准协议的中位总和为29；标准 - NLP协议的中位总和为29.5。参与者使用NLP - NLP协议时可用性测量的平均总分是36.7，而使用标准 - 标准协议时为30.3。
在本研究中，证明了一种将NLP应用于转录听写的EHR数据采集方法的可行性。这种基于听写的新方法有可能减少文档记录所需时间并提高可用性，同时保持文档记录质量。未来的研究将在临床环境中评估基于NLP的EHR数据采集方法。可以合理地断言，EHR将越来越多地使用诸如MediSapien NLP之类的基于NLP的数据录入工具，因为它们有望改善文档记录过程和最终用户体验。
