Natural Language Processing-Enabled and Conventional Data Capture Methods for Input to Electronic Health Records: A Comparative Usability Study.
Natural Language Processing-Enabled and Conventional Data Capture Methods for Input to Electronic Health Records: A Comparative Usability Study.
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DOI：
10.2196/medinform.5544
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发表时间：
2016-10-28
影响因子：
3.2
通讯作者：
Maisel JM
中科院分区：
文献类型：
--
作者：
Kaufman DR;Sheehan B;Stetson P;Bhatt AR;Field AI;Patel C;Maisel JM
众所周知，电子健康记录（EHR）中的文档处理是耗时、低效和繁琐的。听写与人工抄写相结合已成为越来越普遍的做法。近年来，支持自然语言处理（NLP）的数据捕获已成为数据输入的可行替代方案。它使临床医生能够保持对过程的控制，并可能减轻记录负担。问题仍然是这个支持NLP的工作流将如何影响EHR可用性，以及它是否可以满足结构化数据和其他EHR需求，同时增强用户体验。本研究的目的是评估的比较有效性的NLP启用的数据捕获方法，使用听写和数据提取转录文档（NLP条目）的文档时间，文档质量和可用性与标准的EHR键盘和鼠标数据输入。这项形成性研究调查了使用EHR数据采集的NLP Entry和标准Entry方法（“协议”）的4种组合的结果。我们比较了一种新的基于听写的协议，使用MediSapien NLP（NLP-NLP）的结构化数据捕获与标准的结构化数据捕获协议（标准-标准）以及2种新的混合协议（NLP-标准和标准-NLP）。31名参与者包括神经科医生、心脏科医生和肾病科医生。参与者使用4个文档协议生成4个咨询或入院记录。我们记录了任务时间、文档质量（使用医师文档质量工具PDQI-9）和文档流程的可用性。在3个主题领域共记录了118条注释。NLP-NLP方案要求每份心脏病学记录的中位数为5.2分钟，每份肾脏病学记录的中位数为7.3分钟，每份神经病学记录的中位数为8.5分钟，相比之下，使用标准-标准方案的中位数分别为16.9、20.7和21.2分钟，使用标准-NLP方案的中位数分别为13.8、21.3和18.7分钟（2种混合方法中的1种）。使用PDQI-9仪器测量的9个特征中的8个，NLP-NLP协议获得的中位质量得分总和为24.5;标准-标准协议获得的中位质量得分总和为29;标准-NLP协议获得的中位质量得分总和为29.5。当参与者使用NLP-NLP协议时，可用性测量的平均总分为36.7，而使用标准-标准协议时为30.3。在这项研究中，EHR数据捕获的方法，包括应用NLP转录听写的可行性进行了论证。这种新颖的基于听写的方法有可能减少文档所需的时间，并在保持文档质量的同时提高可用性。未来的研究将在临床环境中评估基于NLP的EHR数据捕获方法。有理由断言，EHR将越来越多地使用支持NLP的数据输入工具，如MediSapien NLP，因为它们有望增强文档处理和最终用户体验。
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.
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