Rehm Gregory B, Kuhn Brooks T, Delplanque Jean-Pierre, Guo Edward C, Lieng Monica K, Nguyen Jimmy, Anderson Nicholas R, Adams Jason Y
Department of Computer Science, University of California at Davis, Davis, CA, USA.
Division of Pulmonary, Critical Care, and Sleep Medicine, University of California at Davis, Davis, CA, USA.
J Am Med Inform Assoc. 2018 Mar 1;25(3):295-299. doi: 10.1093/jamia/ocx116.
Lack of access to high-frequency, high-volume patient-derived data, such as mechanical ventilator waveform data, has limited the secondary use of these data for research, quality improvement, and decision support. Existing methods for collecting these data are obtrusive, require high levels of technical expertise, and are often cost-prohibitive, limiting their use and scalability for research applications. We describe here the development of an unobtrusive, open-source, scalable, and user-friendly architecture for collecting, transmitting, and storing mechanical ventilator waveform data that is generalizable to other patient care devices. The system implements a software framework that automates and enforces end-to-end data collection and transmission. A web-based data management application facilitates nontechnical end users' abilities to manage data acquisition devices, mitigates data loss and misattribution, and automates data storage. Using this integrated system, we have been able to collect ventilator waveform data from >450 patients as part of an ongoing clinical study.
无法获取高频、大量的患者源数据,如机械通气波形数据,限制了这些数据在研究、质量改进和决策支持方面的二次利用。现有的收集这些数据的方法具有侵入性,需要高水平的技术专长,而且成本往往过高,限制了它们在研究应用中的使用和可扩展性。我们在此描述了一种用于收集、传输和存储机械通气波形数据的非侵入性、开源、可扩展且用户友好的架构,该架构可推广到其他患者护理设备。该系统实现了一个软件框架,可自动执行并确保端到端的数据收集和传输。一个基于网络的数据管理应用程序有助于非技术终端用户管理数据采集设备的能力,减少数据丢失和错误归因,并自动进行数据存储。作为一项正在进行的临床研究的一部分,使用这个集成系统,我们已经能够从超过450名患者那里收集通气波形数据。