Fall Intervention: Resources Implementation Essay

Effective implementation of an intervention to prevent falls in the hospitals by nurses would require specific resources in training and equipping them with prerequisite knowledge. The resources needed in this case can be of different types depending on the purpose they are meant to serve (Dolci et al., 2020). The following are some of the resources that could be employed in training.

1. Patient safety experts are required to provide direct face-to-face education on fall risks and mitigation measures nurses need to observe when executing their duties. In addition, the experts may use videos and pictures depicting scenarios in the hospital setting where there are more risks and ways a nurse can avoid them.
2. The education tool vital in distinguishing actual and perceive falls need to be availed and used during the training session. The tools include the fall risk assessment tool, the Hendrick II Fall Risk Model, and St. Thomas’s Risk Assessment Tool.
3. There is also a need to used patient-focused consumer resources in training nurses, such as brochures, pamphlets, and handouts. These resources are vital as they give scenarios where and when falls are expected and avoid them (Clayton et al., 2020). Further, materials are designed to help nurses acquire skills in dealing with actual falls in their line of duty.
4. Availing hospital systems, policies, and procedures that nurses can use to reduce fall incidents are also required. Some of these hospital systems can be in the form of equipment nurses can use when attending patients to avoid falls.

The resources which can be used in a training session to overcome falls incidents in hospitals are many, and the nature of falls would determine the type of tools required. It should be noted that some of the resources might be readily available, but nurses do not know how to use them.

References

Clayton, K., Grimm, J., Olguin, S. M., & Tarrant, T. (2020). Impact of falls in the long-term Care setting: A guide to prevention.Web.

Dolci, E., Schärer, B., Grossmann, N., Musy, S. N., 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), e19516. Web.