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Automated Features, Algorithms, and Technologies of Electronic Early Warning/Track-and-Trigger Systems: Systematic
Sharareh Rostam Niakan Kalhori1,2, Mostafa Haghi3, Masresha Derese Tegegne2
1Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, No.17, Bastani Parizi Alley, Qods Street, Tehran, 1417744361, Iran, 98 2188983025.
Journal of Medical Internet Research
|August 10, 2026
Summary
Electronic early warning systems (EW/TTS) primarily use vital signs for patient monitoring and clinical deterioration detection. While many systems offer data exchange, further standardization is needed to improve accuracy and reliability.
Area of Science:
- Healthcare Technology
- Clinical Informatics
- Patient Monitoring Systems
Background:
- Electronic early warning/track-and-trigger systems (EW/TTS) are vital for patient monitoring and detecting clinical deterioration (CD).
- Understanding the automation levels in current EW/TTS is essential for improving patient care and rapid response activation.
Purpose of the Study:
- To conduct a comprehensive overview and critical assessment of electronic EW/TTS.
- To evaluate automated features, algorithms, and technologies used in EW/TTS for CD detection.
Main Methods:
- Systematic review following PRISMA guidelines, searching PubMed, Web of Science, and Scopus (2010-2025).
- Included studies described EW/TTS in real-world settings for CD detection; excluded manual scoring charts.
- Descriptive narrative approach with quality assessment using Joanna Briggs Institute Critical Appraisal Checklist.
Main Results:
- 43 studies reported EW/TTS, with CD detection as a primary objective in 54.5%.
- Systems primarily used vital signs (62.7%) and featured data analytics (55.8%) and interoperable connectivity (69.8%).
- Automation levels varied, with 41.9% having measured automation; common outcomes included earlier warning and higher accuracy.
Conclusions:
- Current EW/TTS utilize measured automation and focus on patient monitoring, with significant data exchange capabilities.
- Evidence is limited by inconsistent metrics and reporting; clinically validated wearables and standardized data frameworks could enhance system reliability.