Purpose
Stroke is a leading cause of morbidity and mortality worldwide, and rapid early detection is essential. Although screening tools such as FAST (face, arm, speech, and time) are widely used, they may miss posterior strokes, whereas BE-FAST (balance-eyes, face, arm, speech, and time) demonstrates greater sensitivity. However, its implementation in digital formats remains limited. This study aimed to develop, validate, and implement BE-ALERT (balance-eyes-arm weakness-language difficulties-extreme headache-reaction slowed or confusion-time to response) as a community-based early stroke detection application.
Methods
This research and development study consisted of development, validation, diagnostic accuracy testing, and community implementation. The development and validation phase included 160 family caregivers of patients with stroke who were aged ≥18 years. The diagnostic accuracy phase included 500 family caregivers who accompanied consecutive patients with suspected stroke in the emergency department. Family caregivers completed the BE-ALERT assessment while accompanying the patients, and their assessment results were compared with neurologist-confirmed diagnoses.
Results
Validation showed a content validity index of 0.95 and a Cronbach α reliability coefficient of .82. Community implementation among 160 participants was associated with higher stroke knowledge scores (82.1) and stronger intention to seek immediate treatment (4.5). Receiver operating characteristic curve analysis yielded an area under the curve of 0.836, indicating good diagnostic accuracy. BE-ALERT showed a sensitivity of 85.0%, specificity of 82.1%, negative predictive value of 94.5% and System Usability Scale score of 74.
Conclusion
BE-ALERT is a practical, accurate, and well-accepted tool for community-based early stroke detection. It may support stroke screening, public education, and timely treatment-seeking behavior.

PURPOSE
The aim of this study was to investigate the accuracy of infrared temperature measurements compared to axillary temperature in order to detect fever in patients.
METHODS
Studies published between 1946 and 2012 from periodicals indexed in Ovid Medline, Embase, CINAHL, Cochrane, KoreaMed, NDSL, KERIS and other databases were selected using the following key words: "infrared thermometer." QUADAS-II was utilized to assess the internal validity of the diagnostic studies. Selected studies were analyzed through a meta-analysis using MetaDisc 1.4.
RESULTS
Twenty-one diagnostic studies with high methodological quality were included representing 3,623 subjects in total. Results of the meta-analysis showed that the pooled sensitivity, specificity, and area under the curve (AUC) of infrared tympanic thermometers were 0.73 (95% CI 0.70~0.75), 0.92 (95% CI 0.91~0.92), and 0.90, respectively. For axillary temperature readings, the pooled sensitivity was 0.67 (95% CI 0.62~0.73), the pooled specificity was 0.87 (95% CI 0.85~0.90), and the AUC was 0.80.
CONCLUSION
Infrared tympanic temperature can predict axillary temperature in normothermic and in febrile patients with an acceptable level of diagnostic accuracy. However, further research is necessary to substantiate this finding in patients with hyperthermia.

Citations

• Development of heat stress index for healthcare workers with personal protective equipment
  Yudong Mao, Yongcheng Zhu, Xiwen Feng, Zhaosong Fang
  Energy and Buildings.2025; 346: 116150.     CrossRef