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Fall Measurement Tools in Healthcare

Falls can be regarded as one of the common and preventable issues that have an adverse impact on patient outcomes and indicate areas to improve in terms of the quality of provided care. Falls are a specifically serious threat to older patients who are a vulnerable population due to their deteriorating health status and current health issues. The interviewed practitioners stated that reporting was the central tool utilized to measure falls in their facility. Patient self-reports are also used as a complementary source of information on the matter.

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The review of recent literature suggests that this instrument is common across the country as healthcare professionals measure falls based on patients’ and medical staff’s reports. Such reports are analyzed to evaluate the rate of falls and the effectiveness of the existing fall prevention initiatives (Røyset et al., 2019). At that, other measurements, especially when it comes to the analysis of fall incidence or fall prevention, are also used.

For instance, Dolci et al. (2020) assessed the effectiveness of the automated fall detection algorithm. The authors found that the instrument was more effective than such measurements as manual chart review, global trigger tool, and patient self-reported falls (Dolci et al., 2020). Such methods as the Berg Balance Scale are employed to predict falls, but the effectiveness of this tool requires further examination (Lima et al., 2018). The interviewed practitioners mentioned that they are aware of several measures that could be helpful at their facilities, and one of them even addressed the hospital’s management to use one of these instruments. The interviewee was waiting for the response and was optimistic about the outcome. Thus, it is possible to note that reporting and charts are still the most widely utilized fall measurements, but innovative strategies also appear in practice.


Dolci, E., Schärer, B., Grossmann, N., Musy, S. M., Zúñiga, F., Bachnick, S., & Simon, M. (2020). Automated fall detection algorithm with global trigger tool, incident reports, manual chart review, and patient-reported falls: Algorithm development and validation with a retrospective diagnostic accuracy study. Journal of Medical Internet Research, 22(9), 1-10.

Lima, C. A., Ricci, N. A., Nogueira, E. C., & Perracini, M. R. (2018). The Berg Balance Scale as a clinical screening tool to predict fall risk in older adults: A systematic review. Physiotherapy, 104(4), 383-394.

Røyset, B., Talseth-Palmer, B. A., Lydersen, S., & Farup, P. G. (2019). Effects of a fall prevention program in elderly: A pragmatic observational study in two orthopedic departments. Clinical Interventions in Aging, 14, 145-154.

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