Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults.
Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults.
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DOI：
10.2196/44977
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发表时间：
2023-04-20
影响因子：
3.2
通讯作者：
中科院分区：
文献类型：
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作者：
电子健康记录（EHRs）中的临床叙述为预测分析提供了有价值的信息；然而，其自由文本形式难以挖掘和分析临床决策支持（CDS）。大规模临床自然语言处理（NLP）管道的重点是数据仓库应用的回顾性研究工作。在床边实施NLP管道的医疗保健服务仍然缺乏证据。我们的目标是详细描述医院范围内的操作管道，以实现实时nlp驱动的CDS工具，并描述一个以用户为中心的CDS工具设计的实施框架协议。该管道集成了先前训练过的用于筛选阿片类药物滥用的开源卷积神经网络模型，该模型利用EHR笔记映射到统一医学语言系统中的标准化医学词汇。在部署深度学习算法之前，一名医师信息学家审查了100名成人遭遇的样本，对其进行了无声测试。开发了一项最终用户访谈调查，以检查用户对最佳实践警报（BPA）的接受程度，从而为筛选结果提供建议。计划的实施还包括一个以人为中心的设计，用户对BPA的反馈，一个具有成本效益的实施框架，以及一个非劣效性的患者结果分析计划。该管道是一个可重复的工作流，带有共享的伪代码，供云服务在弹性云计算环境中摄取、处理和存储来自主要EHR供应商的健康级别7消息的临床记录。笔记的特征工程使用了一个开源的自然语言处理引擎，这些特征被输入到深度学习算法中，结果作为BPA返回到EHR中。深度学习算法的现场无声测试显示灵敏度为93% (95% CI 66%-99%)，特异性为92% (95% CI 84%-96%)，与已发表的验证研究相似。部署前，各医院委员会都收到了住院病人手术的批准书。进行了五次访谈；他们通知了一份教育传单的发展，并进一步修改了BPA，以排除某些患者，并允许拒绝推荐。管道开发中最长的延迟是由于网络安全审批，特别是由于微软（Microsoft Corp）和Epic Systems Corp .云供应商之间交换受保护的健康信息。在无声测试中，由此产生的管道在提供者在电子病历中输入笔记的几分钟内将BPA提供给床边。实时NLP管道的组成部分详细介绍了开源工具和伪代码，供其他卫生系统进行基准测试。在常规临床护理中部署医疗人工智能系统是一个重要但尚未实现的机会，我们的协议旨在缩小人工智能驱动的CDS实施方面的差距。ClinicalTrials.gov NCT05745480;https://www.clinicaltrials.gov/ct2/show/NCT05745480
The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing NLP pipelines at the bedside for health care delivery. We aimed to detail a hospital-wide, operational pipeline to implement a real-time NLP-driven CDS tool and describe a protocol for an implementation framework with a user-centered design of the CDS tool. The pipeline integrated a previously trained open-source convolutional neural network model for screening opioid misuse that leveraged EHR notes mapped to standardized medical vocabularies in the Unified Medical Language System. A sample of 100 adult encounters were reviewed by a physician informaticist for silent testing of the deep learning algorithm before deployment. An end user interview survey was developed to examine the user acceptability of a best practice alert (BPA) to provide the screening results with recommendations. The planned implementation also included a human-centered design with user feedback on the BPA, an implementation framework with cost-effectiveness, and a noninferiority patient outcome analysis plan. The pipeline was a reproducible workflow with a shared pseudocode for a cloud service to ingest, process, and store clinical notes as Health Level 7 messages from a major EHR vendor in an elastic cloud computing environment. Feature engineering of the notes used an open-source NLP engine, and the features were fed into the deep learning algorithm, with the results returned as a BPA in the EHR. On-site silent testing of the deep learning algorithm demonstrated a sensitivity of 93% (95% CI 66%-99%) and specificity of 92% (95% CI 84%-96%), similar to published validation studies. Before deployment, approvals were received across hospital committees for inpatient operations. Five interviews were conducted; they informed the development of an educational flyer and further modified the BPA to exclude certain patients and allow the refusal of recommendations. The longest delay in pipeline development was because of cybersecurity approvals, especially because of the exchange of protected health information between the Microsoft (Microsoft Corp) and Epic (Epic Systems Corp) cloud vendors. In silent testing, the resultant pipeline provided a BPA to the bedside within minutes of a provider entering a note in the EHR. The components of the real-time NLP pipeline were detailed with open-source tools and pseudocode for other health systems to benchmark. The deployment of medical artificial intelligence systems in routine clinical care presents an important yet unfulfilled opportunity, and our protocol aimed to close the gap in the implementation of artificial intelligence–driven CDS. ClinicalTrials.gov NCT05745480; https://www.clinicaltrials.gov/ct2/show/NCT05745480
影响因子：
3.2
作者：
Park, Eunsoo H.;Watson, Hannah I.;Mehendale, Felicity V.;O'Neil, Alison Q.
通讯作者：
O'Neil, Alison Q.
影响因子：
3.2
作者：
Rybinski M;Dai X;Singh S;Karimi S;Nguyen A
通讯作者：
Nguyen A