NURS-FPX4040 Assessment 3: Evidence-Based Proposal and

Evidence-Based Proposal and Annotated Bibliography on Technology in Nursing

Advances in technology and technological applications have revolutionized operations in various fields, healthcare included. The healthcare sector, in particular, has, over the years, experienced incredible growth in the use of various technology and technological applications. Among such technologies are patient monitoring devices. Patient monitoring devices have been used in various aspects of patient care to give care to patients with various illnesses or those who are at risk of particular conditions. An example of patient monitoring devices is the fall detection systems which are used in monitoring patient groups such as elderly persons to prevent their fall (Wang et al., 2020). Therefore, the purpose of this assignment is to create an annotated bibliography regarding the use of fall detection systems as patient monitoring devices

Rational For Technology Topic and Research Process

Patients need to be safe in the care environment. However, events such as patient falls can lead to adverse outcomes such as bone fractures, broken body parts, and even death. In addition, patient falls also lead to increased spending and prolonged stay in hospitals which lead to hospital-acquired infections (Wang et al., 2020). Therefore, this is a topic of interest; hence, it was chosen to further explore the development in the use of monitoring devices to control the rates of patient falls. A research process was conducted to obtain relevant articles that show the use of these devices and their importance in controlling patient falls. The article databases used include Google Scholar, the Cochrane Database of Systematic Reviews, Ovid, the Cochrane Central Register of Controlled Trials, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Medline. The keywords used include patient falls, fall detection systems, sensors, and patient monitoring systems.

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Annotated Bibliography

Hashim, H. A., Mohammed, S. L., & Gharghan, S. K. (2020). Accurate fall detection for patients with Parkinson’s disease based on a data event algorithm and wireless sensor nodes. Measurement, 156, 107573. https://doi.org/10.1016/j.measurement.2020.107573

This research was conducted to determine the accuracy of the fall detectors for patients with Parkinson’s disease using wireless sensor nodes. Therefore, the research aimed at designing and implementing a wearable fall-detection system based on the wireless sensor network (Hashim et al.,2020). The system accurately detected patient’s falls based on the data event algorithms. Analysis of the data showed that the fall detection system achieved 100% specificity, sensitivity, and accuracy in patient fall detection. This technology is relevant to nursing practice and the work of interdisciplinary teams since it improves nurse efficiency in improving patient outcomes. Interdisciplinary teams can also collaborate to analyze patient data and make efforts to stop falls. This publication was chosen because it directly addresses fall detection and high accuracy. The nurse informaticist can play a critical role in collaboration with other nurses and physicians to improve outcomes.

Ajerla, D., Mahfuz, S., & Zulkernine, F. (2019). A real-time patient monitoring framework for fall detection. Wireless Communications and Mobile Computing, 2019, 1-13.https://doi.org/10.1155/2019/9507938

This article by Ajerla et al. (2019) focuses on a real monitoring framework to detect patient falls. Therefore, the purpose of this research was to formulate a fall detection system that applies computing approaches using wearable devices that send data for real analysis to detect falls. The analysis of the data showed that the patient monitoring device had a positive impact on patient outcomes as it was able to detect patient falls with 99% accuracy. Therefore, the technology used also greatly improved patient safety as fall detection leads to the prevention of patient falls. This technology is also relevant to nursing since nurses can use the described fall detection system to help reduce fall incidences among patients. The work of the interdisciplinary care team can also be positively impacted since they can collaborate to use the system to monitor patient falls and take appropriate measures to prevent such fall incidences. This article was also selected since it addresses the technology of interest, which is a fall detection system. In addition, it is also interesting since the researchers used relatively cheaper materials to make the fall detection system.

Saadeh, W., Butt, S. A., & Altaf, M. A. B. (2019). A patient-specific single-sensor IoT-based wearable fall prediction and detection system. IEEE Transactions On Neural Systems and Rehabilitation Engineering, 27(5), 995-1003. https://doi.org/10.1109/TNSRE.2019.2911602

This article by Saadeh et al.(2019), mainly focused on sensor Internet of Things-based wearable fall prediction and detection systems. Therefore, the authors aimed to explore the efficacy of the system in detecting patient falls and controlling or reducing the rates of falls. The analysis of the experimental data showed that the fall detection and prediction system had a significant impact on patient safety. The researchers noted that the system could detect patient falls by over 99% accuracy. In addition, the system also achieved sensitivity and specificity of 97.8% and 99.1%, respectively, in the fast model. Therefore, the system could efficiently and accurately detect patient falls and help prevent falls, improving patient safety. According to this source, this technology is also relevant to nursing practice since it enhances the care of preventing falls through earlier detection and prediction, which then prompts the nurses to act fast and prevent potential patient falls that could have resulted in an adverse event. It also impacts the work of interdisciplinary healthcare teams as such a team can effectively use the system to improve patient outcomes related to patient falls.

Espinosa, R., Ponce, H., Gutiérrez, S., Martínez-Villaseñor, L., Brieva, J., & Moya-Albor, E. (2019). A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset. Computers In Biology and Medicine, 115, 103520. https://doi.org/10.1016/j.compbiomed.2019.103520

This is another source that addresses the use of fall detection systems to detect potential patient falls and trigger actions to prevent such events. Therefore, these researchers explored the use of a vision-based approach for fall detection. The researchers used several cameras and convolutional neural networks to help detect potential patient falls. The system analyzes images in fixed time windows and extracts features through the use of the optical flow method (Espinosa et al.,2019). The analysis of the data also showed that this technology had an impact on patient care and patient safety. For example, the researcher realized that their multi-vision approach was able to detect human falls and achieve an accuracy of 96%. This shows that the technology was accurate and efficient, hence increasing the possibility of detecting, controlling, and preventing patient falls. According to these sources, this technology is also relevant to nursing practice since it is a technology that can be applied by nurses in the patient care environment to improve patient safety by preventing patient falls. The technology also has an impact on the work of interdisciplinary healthcare teams since a team caring for patients such as older patients can collaborate to use this system and reduce rates of patient falls. This publication was also selected since it showed a high accuracy of the technology in detecting patient falls, which is also key in attempts to reduce patient fall rates.

Summary and Recommendations

The four selected articles were carefully chosen since they adequately address fall monitoring systems as part of patient monitoring systems. Therefore, various key learnings were obtained from the four publications. For example, the use of fall detection systems can greatly reduce fall incidences and improve patient outcomes. Besides, the efficacy of these systems in detecting patient falls largely depends on their accuracy and specificity. Various organizational factors also influence the selection of a technology in healthcare. One of them is organizational policy. Organizations having policies that focus on patient safety and a better patient environment will embrace healthcare technologies that eliminate potential hazards, such as those that can lead to falls or medication errors.

The implementation of a fall detection system in healthcare settings can greatly improve patient outcomes. For example, they can be used to help reduce fall incidences among elderly and frail patients since they are more prone to falls(Hashim et al.,2020). The fall detection system can help detect falls, which allows nurses to act swiftly and prevent falls that could otherwise have led to adverse events. This technology should, therefore, be implemented in the setting since it improves the efficiency of the organization in caring for this population. It also leads to improved outcomes since falls are prevented, hence protecting patients from injuries that could have led to broken body parts, prolonged hospital stays, higher healthcare spending, and litigations. It can also improve the productivity of interdisciplinary teams and improve their satisfaction, hence higher rates of staff retention.

Conclusion

This write-up has explored fall detection systems as a type of patient monitoring system. Four articles have been annotated, and they show the efficacy of these systems in detecting patient falls. In addition, these systems can improve patient safety satisfaction and also enhance the efficiency of an interdisciplinary team.

References

Wang, X., Ellul, J., & Azzopardi, G. (2020). Elderly fall detection systems: A literature survey. Frontiers in Robotics and AI, 7, 71. https://doi.org/10.3389/frobt.2020.00071

NURS-FPX4040 Assessment 3: Evidence-Based Proposal and Annotated Bibliography on Technology in Nursing

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