Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany.
Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
PLoS One. 2023 Mar 15;18(3):e0283010. doi: 10.1371/journal.pone.0283010. eCollection 2023.
This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD).
This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers.
This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences.
Systematic review registration: PROSPERO CRD42022334988.
本系统评价方案旨在确定电子预警/跟踪和分诊系统（EW/TTS）中用于预测临床恶化（CD）的自动特征、应用技术和算法。
本研究将使用 PubMed、Scopus 和 Web of Science 数据库评估 EW/TTS 的特征，包括其自动化特征、技术和算法。为此，我们将纳入任何没有时间限制的报告 EW/TTS 的英文文章。检索到的记录将由两名作者独立筛选，并从研究中提取相关数据进行进一步分析。纳入的文章将由两名研究人员使用 JBI 批判性评估清单进行独立评估。
本研究旨在确定电子版本的 EW/TTS 中的可用自动化特征，以阐明所应用的技术、系统的自动化水平和所使用的算法，为实现完全自动化的 EW/TTS 铺平道路，这是预防 CD 及其不良后果的潜在解决方案之一。
系统评价注册：PROSPERO CRD42022334988。
