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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.
Background:
Electronic early warning/track-and-trigger systems (EW/TTS) are crucial for patient monitoring, detecting clinical deterioration (CD), and activating rapid response teams. Understanding the current level of automation in EW/TTS is essential.
Objective:
This study aimed to provide a comprehensive overview and critical assessment of electronic EW/TTS, including automated features, algorithms, and technologies, following a published registered study protocol.
Methods:
Based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we included studies from PubMed, Web of Science, and Scopus published between January 2010 and December 2025 describing EW/TTS applied in real-world settings, and electronic systems for CD detection. We excluded studies outside the clinical context or those that used manual scoring charts. We applied a descriptive narrative approach and a methodological quality assessment according to the Joanna Briggs Institute Critical Appraisal Checklist.
Results:
After removing outliers and duplicates, the query returned 1181 studies. The selected studies (n=43) reported CD as the primary objective in 24 of 44 (54.5%) reported primary objectives, with ICU transfer in 16 of 68 (23.5%) reported secondary objectives, and mortality prediction in 10 of 68 (14.7%) reported secondary objectives. EW/TTS primarily relied on vital signs and assessment scores, accounting for 42 of 67 (62.7%) reported clinical indexes to detect and predict CD effectively. Among the included systems, 18 of 43 (41.9%) had a measured automation level, 11 of 43 (25.6%) had a managed automation level, and 7 of 43 (16.3%) had a defined automation level. The studies focused on several technological domains, with a strong emphasis on data analytics (24/43, 55.8%) and hardware technologies (7/43, 16.3%). Predictive algorithms, including statistical and machine learning approaches, were used in 11 of 43 (25.6%) systems. Interoperable connectivity was reported in 30 of 43 (69.8%) systems, including connectivity with electronic health records, wearable devices, and communication platforms such as Ascom Unite, as well as integrations using standards such as Health Level Seven Fast Healthcare Interoperability Resource and Health Level Seven. Evaluations of the systems showed earlier warning (14/70, 20%), higher accuracy (12/70, 17.1%), and lower specificity (9/70, 12.9%) as the main reported outcomes. Electronic EW/TTS were most prevalent in the United States (15/43, 34.9%), the United Kingdom (6/43, 14%), and the Netherlands (6/43, 14%).
Conclusions:
Current EW/TTS systems implemented a measured level of automation and primarily focused on patient monitoring in hospital surgery wards. More than half of EW/TTS featured data exchange capabilities and connectivity with other systems. Reported outcomes of EW/TTS included early warning, high accuracy, and lower specificity. However, the included evidence was limited by heterogeneous prediction targets, inconsistent performance metrics and time horizons, and poor reporting of development history and system failure. Using clinically validated wearable devices and establishing a standardized data collection framework may further improve system accuracy and reliability.
