The promotion of safety, quality, and efficiency is crucial for patients’ optimum health and wellbeing in nursing practice. Health issues such as falls affect the realization of these outcomes. Falls affect hospitalized patients disproportionately. For example, the risk and rates of falls among elderly patients is high as compared to other patient populations. Falls increase the risk of adverse outcomes such as unintended injuries, increased care costs, prolonged hospitalization, and premature mortalities. Novel solutions can be utilized to detect and prevent falls. However, they are rarely utilized in most healthcare institutions because of issues such as lack of provider competencies and resource-related challenges. Therefore, this project proposed the use of automated fall prevention and detection technologies in reducing falls among hospitalized elderly patients. The PICOT for the project is, among hospitalized patients aged 65 year and above at risk of falls (P), does the use of automated fall detectors (I), as compared to usual care (C), reduce the rates of falls by 50% (O) within six months (T)?
Search Methods
A search for relevant literature for the project was performed in several databases. They included EMBASE, CINAHL, PubMEd, and Google Scholar. The search terms that were utilized in the project included automated fall detection technologies, automated fall prevention technologies, effectiveness of automated fall detection and prevention technologies, and patient outcomes in automated fall detection and prevention technologies. The criteria that were adopted included assessing the articles to ensure they addressed the topic, relevance to nursing and healthcare, and if they were published over the last five years. The articles must have also been authored in the English language.
Synthesis of Literature
Ten articles were included in this review. Coahran et al. (2018) conducted a qualitative study involving nurses working in two geriatric psychiatry units in a regional mental health hospital to examine their experiences with HELPER system. Interviews were conducted with the psychiatric nurses, transcribed, and analyzed thematically using naturalistic inquiry approach. The findings showed that nurses had positive experience with the system, as it had high accuracy rate in fall detection and prevention. The article supports the feasibility of using fall detection and prevention technologies in my practice site.
Gaspar and Lapão (2021) conducted a quantitative, systematic review that determine the clinical applicability of ehealth devices in assessing and treating people with balance disorders. A search performed I databases that included Embase, SciELO, and PubMed yielded 21 articles used in this research. The study findings showed that eHealth devices improved care outcomes, including reducing risk of falls among patients with balance disorder. The research supports the enhanced benefits of fall detection and prevention technologies in promoting patient safety and quality.
Joshi et al. (2022) conducted a qualitative study that examined the perceptions of surgical team members towards the use of wearable sensors for surgical patients. Semi-structured interviews were conducted with 48 senior and junior surgeons and senior and junior nurses to obtain their insights on technology use. The interviews were audio-recorded and transcribed verbatim. The results showed that wearable sensors for continuous monitoring were effective in improving patient safety. The participants also had positive experiences with their use, hence, supporting the need for the technology for fall prevention in my practice.
Maneeprom et al. (2019) conducted a quantitative study to examine the effectiveness of a robotic fall prevention program on knowledge, exercises, balance, and fall incidence among elderly in senior housings. The researchers used 64 elderly patients. Two-tailed statistical hypothesis was adopted for sample size calculation with senior housings selected using purposive sampling. The results of this study showed a statistically significant improvement in knowledge, number of exercises, and falls in the intervention group as compared to the control group. This article supports the PICOT by showing the enhanced benefits of utilizing fall detection and prevention technologies in healthcare.
Mulas et al. (2021) conducted quantitative research that verified the feasibility of wearable inertial sensors for gait and mobility disorders in Italian elderly persons. The researchers used 213
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