International Journal of Clinical Case Reports, 2025, Vol.15, No.5, 200-208 http://medscipublisher.com/index.php/ijccr 206 artificial intelligence and user-centered design, in order to assess the fall risk of the elderly more comprehensively and accurately (Yu et al., 2025). Without such a system, fall prevention might remain fragmented and its practical effectiveness in the community would also be limited. 7 Concluding Remarks The commonly used fall risk assessment methods at present, such as Timed Up and Go (TUG) test, Berg balance scale and fall history, are practical and easy to operate in the community. However, their predictive effects are average, and the area under the curve (AUC) is mostly lower than 0.7. No single method can maintain a high level of accuracy in all populations, so clinical judgment remains very important when identifying high-risk groups among the elderly in the community. To better prevent falls in community care, it is recommended to use short and effective tools, such as the fall history or the "Stay Independent" manual, and combine them with the clinical judgment of professionals. These methods have relatively low demands for resources and training and are suitable for large-scale application. However, medical staff should still be aware of the shortcomings of each tool and, on the basis of assessment, cooperate with targeted intervention and continuous follow-up. Future research should focus on developing and validating multi-factor, easy-to-use assessment tools that can integrate sensor technology, artificial intelligence and multiple risk factors. The effectiveness of such new methods still needs to be verified through large-scale prospective studies to ensure their applicability, accuracy and feasibility, so that they can be widely used among different community populations. Acknowledgments The author extends sincere thanks to Dr An for her feedback on the manuscript. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Alharbi A., 2023, Comparison of three fall risk assessment tools in community-dwelling saudi elderlies, Majmaah Journal of Health Sciences, 11(4): 1445. https://doi.org/10.5455/mjhs.2023.04.004 Argyrou C., Dionyssiotis Y., Galanos A., Vlamis J., K.Triantafyllopoulos I., Dontas I., and Chronopoulos E., 2023, Fall risk question-based tools for fall screening in community-dwelling older adults: a systematic review of the literature, Journal of Frailty Sarcopenia and Falls, 8: 240-253. https://doi.org/10.22540/JFSF-08-240 Bravo J., Rosado H., Tomás-Carús P., Carrasco C., Batalha N., Folgado H., and Pereira C., 2021, Development and validation of a continuous fall risk score in community-dwelling older people: an ecological approach, BMC Public Health, 21(Suppl 2): 808. https://doi.org/10.1186/s12889-021-10813-w Cai W.P., Huang Z.M., Han Q.X., and Huang Y.L., 2025, Research on the chronic disease management model for the elderly based on a community health assessment system, International Journal of Clinical Case Reports, 15(1): 44-51. https://doi.org/10.5376/ijccr.2025.15.0005 Chalke A., Leito G., Sidhu A., McClelland J., Agyemang S., Luzingu J., Agarwal N., Steckler L., Wu A., and Chen Z., 2025, Practice and impact of using fall screening tools in emergency medicine for older adults: a scoping review, Journal of Applied Gerontology, 12: 07334648251315279. https://doi.org/10.1177/07334648251315279 Chen M., Wang H., Yu L., Yeung E., Luo J., Tsui K., and Zhao Y., 2022, A systematic review of wearable sensor-based technologies for fall risk assessment in older adults, Sensors, 22(18): 6752. https://doi.org/10.3390/s22186752 Chen P., Lin H., Ong J.R., and Ma H., 2020, Development of a fall-risk assessment profile for community-dwelling older adults by using the National Health Interview Survey in Taiwan, BMC Public Health, 20(1): 234. https://doi.org/10.1186/s12889-020-8286-8 Chen X., He L., Shi K., Wu Y., Lin S., and Fang Y., 2023a, Interpretable machine learning for fall prediction among older adults in China, American Journal of Preventive Medicine, 65(4): 579-586. https://doi.org/10.1016/j.amepre.2023.04.006 Chen X., Lin S., Zheng Y., He L., and Fang Y., 2023b, Long-term trajectories of depressive symptoms and machine learning techniques for fall prediction in older adults: evidence from the china health and retirement longitudinal study (CHARLS), Archives of Gerontology and Geriatrics, 111: 105012. https://doi.org/10.1016/j.archger.2023.105012
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