NURS FPX 8030 Assessment 4 Methods and Measurement

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Capella university

NURS-FPX 8030 Evidence-Based Practice Process for the Nursing Doctoral Learner

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Date

Methods and Measurement

Patient falls are an important issue that affects patient safety in medical settings. 30-35% of falling incidents cause injuries, resulting in a 6.3-day rise in hospital stays and a $4,000 expense per incidence (Mikos et al., 2021). Managing patient falls among older persons in acute care settings is an urgent issue in healthcare settings, which is frequently compounded by insufficient fall prevention strategies among healthcare personnel in hospitals. In Henry Ford Hospital (HFH), inpatient falls are a serious concern as hospital safety rating statistics of 2023 indicate a 0.164 rate of inpatient falls per 1000 patients, which requires improvement to meet the national benchmark data (Leapfrog, 2024).

This assessment assesses the efficiency of comprehensive fall avoidance bundle interventions using the Fall Risk Assessment Tool and Fall Risk Management Information System. These instruments were selected for their proven validity and supporting methods, which provide a solid foundation for assessing and improving fall prevention practice conformity. The purpose of using these technologies is to mitigate falls among older inpatients and enhance patient safety outcomes in healthcare settings based on the PICO(T) question.

Instruments Used for the Effectiveness of Interventions

Patient safety issues, such as elderly patient falls, demand the efficient intervention of a patient fall prevention bundle to address the PICO(T) question: In an acute care unit, would a standard patient fall prevention bundle, in comparison to current prevention practices, lower fall incidences in elderly adults within 12 weeks? It is crucial to assess the efficacy of interventions in addressing the safety issue and bringing practice change in the hospital setting. The effectiveness of intervention can be analyzed using tools that offer qualitative and quantitative data regarding fall risks, rate of patient falls, and intervention compliance rate through monitoring and feedback. The selected tools are discussed below:

Fall Risk Assessment Tool

The Fall Risk Assessment Tool (FRAT), which includes the Morse Fall Scale (MFS), is an excellent tool for evaluating the efficacy of fall prevention interventions that focus on lowering fall rates in older people. MFS was created in 1989 by Janice Morse, a nurse researcher and professor. It was established to offer an easy, accurate way of predicting fall risk in medical settings. The MFS’s quantitative methodology enables systematic data gathering, helping the detection of fall risk and trends with time. The MFS employs a numerical scoring method in which different risk factors are allocated distinct scores (Ji et al., 2023).

Numerous studies have proven its validity and accuracy, and it is highly reliable. This reliability guarantees that the data gathered is trustworthy and can be utilized to draw informed decisions about the efficacy of fall prevention interventions. It examines aspects such as past experiences of falling, second diagnosis, mobility aids, IV therapy, posture, and mental state (Mousavipour et al., 2022). Medical staff can monitor changes in fall risk by using the MFS on a regular basis before and after applying the fall prevention bundle. A drop in score indicates the effectiveness of the intervention.

Fall Risk Management Information System

The Electronic Health Record (EHR) integrated Fall Risk Management Information System (FRMIS) is another useful tool for evaluating the efficacy of patient preventive measures. It has various functions, including fall incidence reporting and surveillance, risk assessment, and patient and staff feedback. Eclipse developed FRMIS, which uses cutting-edge technology to assess fall prevention practices in real-time (Eclipse, 2024). The real-time ability of FRMIS to gather data, assess, and offer feedback makes it an effective tool for ensuring consistent improvement in fall prevention practices, including education, risk analysis, environmental modification, and assistive tools.

It monitors the execution and outcomes of fall risk evaluations over time to determine whether interventions are effective in lowering fall events (Wabes et al., 2024). Dashboards provide visual feedback on adherence rates, fall incidences, and intervention outcomes, allowing for quicker and more informed decisions. The constant monitoring capacity is helpful in high-risk areas, such as specific bed positions where prompt intervention execution and adherence are critical for preventing patient falls (Wang et al., 2024). 

The reliability of FRMIS is demonstrated through qualitative and quantitative data assessment. It gathers quantitative information about fall events, such as the rate and severity. By reviewing this data, medical professionals can determine whether fall avoidance interventions are lowering the occurrence of falls and improving patient safety. Feedback from patients and clinicians gives insights into the reported effectiveness of fall prevention bundle intervention, and any impediments to execution. This qualitative data is useful in identifying areas for advancement. Implementing FRMIS can significantly enhance fall prevention interventions and assist caregivers in preventing falls among hospitalized patients (Wang et al., 2024).

Relevant Studies

Several relevant studies in the literature demonstrated the reliability and efficacy of instruments used to evaluate the efficacy of fall avoidance interventions and adherence. Guo et al. (2022), performed a significant study using the MFS, which is FRAT. The purpose of this research was to assess the influence of a fall prevention approach that included patient education and risk assessment.

Using MFS, the researchers carefully tracked and observed the fall risk before and after the prevention intervention. The results showed that after implementing an intervention among old patients, the falling number dropped from three to zero, and the Knowledge-Attitude-Practice (KAP) score increased. Furthermore, Dyke et al. (2020), exhibited the effectiveness of MFS in acquiring precise risk assessment data and also the efficacy of nurse-led fall prevention interventions, including patient education. It highlighted the favorable effects of focused fall prevention interventions. The results revealed a 34% decrease in injurious falls among elderly patients.

The research by Wang et al. (2024) evaluated the FRMIS’s effectiveness in managing inpatient falls and increasing patient fall prevention practices among medical personnel. This study used FRMIS tools in a hospital context, allowing for continuous tracking and feedback. The findings revealed that the system provided medical professionals with fast notifications and fall occurrence reporting, hence increasing patient risk analysis, which is an important part of the fall avoidance intervention bundle. Furthermore, the research by Wabes et al. (2024), identified a significant reduction in patient falls, demonstrating the system’s ability to improve patient safety. This study emphasizes the practical uses of EHR-integrated FRMIS in supporting persistent changes in fall prevention intervention through ongoing prediction of risks, surveillance of patients and risk factors, and patient and staff feedback regarding intervention. 

The Rationale of Selection of Studies

The rationale of the studies demonstrates their relevance to the problem or PICO(T) question and intervention for the improvement project. The MFS and EHR-integrated FRMIS are selected as a tool to assess the efficacy of fall avoidance bundle interventions due to their established validity, accuracy, and complementing methods. The MFS provides a strong framework for systematically evaluating and analyzing patient fall risk, which is critical for developing baseline preventative strategies and tracking progress over time. Its high credibility is shown in research by Guo et al. (2022), stating the efficiency of MFS in assessing fall prevention practices.

This study is similar to the PICO(T) question project in that it focuses on elderly patients and implements a fall prevention approach. Furthermore, Dyke et al. (2020) present constant data collection through MFS, which makes it suited for recurrent evaluations of risks in fall prevention programs. On the other hand, EHR-integrated FRMIS offers a technological edge by delivering real-time input and continuous alarms, reporting, and monitoring, which is crucial for improving immediate compliance to fall prevention practices as demonstrated by Wang et al. (2024), that it offers comprehensive patient risk assessment and efficacy of interventions.

The study is similar in that it addresses the patient fall issue in health settings and adopts similar tools. However, it does not outline a particular intervention. Moreover, the study of Wabes et al. (2024), is consistent and similar due to using similar tools for monitoring and prediction of elderly patient falls. The dissimilarity between the project or PICO (T) question and study lies in a different context, which is the aged care setting.  

Summary

Addressing the patient fall problem through fall prevention strategies in health settings is crucial to improving patient outcomes. The MFS and the EHR-integrated FRMIS are useful and complementing tools for assessing the efficacy of fall prevention avoidance bundle interventions for older patients in acute care settings. The systematic and quantitative fall risk observations supplied by MFS, coupled with the continual tracking, assessment, and input capabilities of EHR, integrate FRMIS, establishing an integrated strategy to improve fall prevention intervention and lower inpatient fall rates in hospitals such as HFH. The choice of these tools is reinforced by findings from the literature, illustrating their usefulness in prior research. Applying these tools to assess the effectiveness and performance of intervention in medical facilities can significantly improve inpatient fall prevention efforts and produce improved patient safety results.

References

Dykes, P. C., Burns, Z., Adelman, J., Benneyan, J., Bogaisky, M., Carter, E., Ergai, A., Lindros, M. E., Lipsitz, S. R., Scanlan, M., Shaykevich, S., & Bates, D. W. (2020). Evaluation of a patient-centered fall-prevention tool kit to reduce falls and injuries. Journal of American Medical Association Network Open, 3(11), e2025889–e2025889. https://doi.org/10.1001/jamanetworkopen.2020.25889

Eclipse. (2024). Risk management. eclipsesuite.com. https://www.eclipsesuite.com/solutions/risk-management/

NURS FPX 8030 Assessment 4 Methods and Measurement

Guo, X., Wang, Y., Wang, L., Yang, X., Yang, W., Lu, Z., & He, M. (2022). Effect of a fall prevention strategy for the older patients: A quasi‐experimental study. Nursing Open, 10(2), 1116–1124. https://doi.org/10.1002/nop2.1379

Ji, S., Jung, H.-W., Kim, J., Kwon, Y., Seo, Y., Choi, S., Oh, H. J., Baek, J. Y., Jang, I.-Y., & Lee, E. (2023). Comparative study of the accuracy of at-point clinical frailty scale and Morse fall scale in identifying high-risk fall patients among hospitalized adults. Annals of Geriatric Medicine and Research, 27(2), 99–105. https://doi.org/10.4235/agmr.23.0057

Leapfrog. (2024). Henry Ford Hospital- Hospital details table. Hospitalsafetygrade.org. https://www.hospitalsafetygrade.org/table-details/henry-ford-hospital

Mikos, M., Banas, T., Czerw, A., Banas, B., Strzępek, L., & Curyło, M. (2021). Hospital inpatient falls across clinical departments. International Journal of Environmental Research and Public Health/International Journal of Environmental Research and Public Health, 18(15), 8167–8167. https://doi.org/10.3390/ijerph18158167

Mousavipour, S. S., Ebadi, A., Saremi, M., Jabbari, M., & Khorasani-Zavareh, D. (2022). Reliability, sensitivity, and specificity of the morse fall scale: A hospitalized population in Iran. Archives of Trauma Research, 11(2), 65-70. https://doi.org/10.4103/ATR.ATR_7_22

Wabe, N., Meulenbroeks, I., Huang, G., Silva, S. M., Gray, L. C., Close, J. C., & Westbrook, J. I. (2024). Development and internal validation of a dynamic fall risk prediction and monitoring tool in aged care using routinely collected electronic health data: A landmarking approach. Journal of the American Medical Informatics Association, 31(5), 1113-1125. https://doi.org/10.1093/jamia/ocae058

NURS FPX 8030 Assessment 4 Methods and Measurement

Wang, Y., Jiang, M., He, M., & Du, M. (2024). Design and implementation of an inpatient fall risk management information system. Journal of Medical Internet Research Medical Informatics, 12, e46501–e46501. https://doi.org/10.2196/46501

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