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 remain a significant safety issue within healthcare environments, posing risks to patient well-being and contributing to extended hospitalizations and increased medical expenses. Approximately 30–35% of fall incidents result in injury, leading to an average 6.3-day increase in hospital stays and about \$4,000 in additional costs per event (Mikos et al., 2021). Preventing falls among elderly patients in acute care settings presents an ongoing challenge, often worsened by gaps in fall prevention practices among healthcare staff. At Henry Ford Hospital (HFH), patient falls continue to be a major safety concern, with 2023 hospital safety data reporting an inpatient fall rate of 0.164 per 1,000 patient days—highlighting a need for improvement to align with national benchmarks (Leapfrog, 2024). This quality improvement assessment examines the effectiveness of a structured fall prevention bundle using two validated tools: the Fall Risk Assessment Tool and the Fall Risk Management Information System. These tools were selected due to their demonstrated reliability and methodological soundness, offering a solid basis for evaluating adherence to fall prevention practices and enhancing patient safety outcomes in response to the PICO(T) inquiry.

Instruments for Evaluating Intervention Effectiveness

Preventing falls, particularly in older hospitalized patients, necessitates evidence-based strategies guided by reliable assessment tools. Addressing the PICO(T) question—whether implementing a standardized fall prevention bundle in an acute care unit, compared to existing practices, reduces fall rates in elderly patients within a 12-week timeframe—requires precise tools to monitor intervention outcomes. The selected tools offer both qualitative and quantitative insights into fall risk levels, fall incidences, and adherence rates, making them suitable for intervention monitoring and feedback collection.

Fall Risk Assessment Tool

The Fall Risk Assessment Tool (FRAT), incorporating the Morse Fall Scale (MFS), is a widely accepted instrument for assessing fall prevention initiatives targeting elderly patient populations. Developed by Janice Morse in 1989, the MFS provides a straightforward and dependable system for evaluating fall risk in healthcare settings. Its quantitative structure enables healthcare providers to systematically collect and analyze data on fall risk factors over time (Ji et al., 2023). The MFS allocates numerical scores to variables such as prior fall history, secondary diagnoses, use of mobility aids, IV therapy, gait, and cognitive status (Mousavipour et al., 2022). Its high validity and reliability ensure that the data collected is trustworthy and actionable. Repeated assessments before and after the implementation of a fall prevention bundle allow clinical staff to track risk level changes, with declining scores indicating improved patient safety outcomes.

Fall Risk Management Information System

Another critical resource is the Electronic Health Record (EHR)-integrated Fall Risk Management Information System (FRMIS), which enhances fall prevention efforts by offering real-time monitoring, risk assessment, incident reporting, and feedback. Developed by Eclipse, FRMIS integrates advanced technology to continuously track fall prevention practices and outcomes (Eclipse, 2024). Its real-time data collection and reporting features enable healthcare teams to swiftly assess intervention compliance, conduct environmental modifications, and implement patient-specific safety measures. The system’s dashboards visually represent adherence rates, fall incidents, and intervention results, facilitating faster, evidence-based decision-making (Wabes et al., 2024). This ongoing monitoring is especially valuable in high-risk hospital areas, where immediate adherence to fall prevention protocols is critical (Wang et al., 2024).

FRMIS demonstrates both quantitative and qualitative value in evaluating fall prevention strategies. It records numerical data on fall frequencies and severity while capturing feedback from staff and patients regarding the intervention’s perceived effectiveness and implementation barriers. This combination of data enables comprehensive performance evaluations, identifying areas for improvement and supporting consistent patient safety enhancements (Wang et al., 2024).

Related Studies

Several research studies confirm the reliability and practical application of the selected instruments for evaluating fall prevention interventions. Guo et al. (2022) conducted a quasi-experimental study using the MFS to assess the effects of a fall prevention strategy combining patient education and risk evaluation. Fall risks were measured before and after implementing the prevention intervention, with outcomes indicating a decrease in fall incidents from three to zero, along with improvements in patient knowledge, attitudes, and practices (KAP scores). Similarly, Dykes et al. (2020) demonstrated the MFS’s effectiveness in facilitating accurate risk assessments and supporting nurse-led fall prevention initiatives, noting a 34% reduction in fall-related injuries among elderly patients following targeted interventions.

Furthermore, research by Wang et al. (2024) explored the impact of FRMIS in managing inpatient falls and enhancing safety practices among healthcare providers. The system’s integration enabled continuous fall risk tracking and immediate incident reporting, contributing to improved fall prevention performance. Wabes et al. (2024) further validated FRMIS’s effectiveness, identifying significant reductions in patient fall rates and underscoring the system’s capacity to support sustained safety improvements through proactive risk identification and real-time feedback mechanisms.

Rationale for Study Selection

The studies referenced were selected based on their direct relevance to the identified patient safety issue and alignment with the PICO(T) question. The Morse Fall Scale (MFS) and EHR-integrated FRMIS were chosen due to their well-established validity, reliability, and complementary evaluation capabilities. The MFS facilitates systematic, quantitative fall risk assessments, serving as a foundation for baseline risk identification and ongoing monitoring. Its effectiveness is supported by Guo et al. (2022), whose study parallels the current project by focusing on elderly patients and incorporating a structured fall prevention protocol. Additionally, Dykes et al. (2020) highlighted the MFS’s utility in continuous risk assessment, making it ideal for iterative evaluations.

Conversely, the EHR-integrated FRMIS adds a technological advantage by enabling real-time risk monitoring, incident reporting, and feedback. Wang et al. (2024) demonstrated its value in enhancing immediate adherence to fall prevention protocols, mirroring the project’s focus on inpatient falls. While this study addressed fall prevention broadly without specifying a particular intervention, it remains applicable due to its focus on fall risk management systems. Lastly, Wabes et al. (2024) confirmed FRMIS’s capacity for ongoing risk prediction and monitoring in healthcare environments. Although conducted in aged care settings, the findings remain relevant for acute care environments given the consistent use of similar risk management technologies.

Summary

Effective fall prevention strategies are essential for enhancing patient safety in hospital settings, particularly among older patients. The Morse Fall Scale (MFS) and the EHR-integrated Fall Risk Management Information System (FRMIS) are valuable tools for evaluating the effectiveness of fall prevention bundle interventions in acute care environments. By combining systematic, quantitative risk assessments with continuous monitoring, feedback, and intervention compliance tracking, these tools support a comprehensive, data-driven approach to reducing inpatient fall rates. Literature evidence affirms the effectiveness of these instruments, validating their use in quality improvement initiatives aimed at enhancing patient safety outcomes in institutions such as Henry Ford Hospital.

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. JAMA Network Open, 3(11), e2025889–e2025889. https://doi.org/10.1001/jamanetworkopen.2020.25889

NURS FPX 8030 Assessment 4 Methods and Measurement

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

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, 18(20), 10795. https://doi.org/10.3390/ijerph182010795

Wabes, A. M., Mohamed, A. A., El-Aziz, N. A., & Eladl, A. M. (2024). Application of electronic fall prevention programs in long-term care hospitals: Effect on patient safety and nursing performance. Nursing Open, 11(1), 221–230. https://doi.org/10.1002/nop2.1694

NURS FPX 8030 Assessment 4 Methods and Measurement

Wang, Y., Zhang, Q., Guo, X., & Li, X. (2024). Application effect of electronic medical record-based fall risk management system in inpatient wards: A quasi-experimental study. Nursing Open, 11(1), 310–318. https://doi.org/10.1002/nop2.1712