NURS FPX 6016 Assessment 2 Quality Improvement Initiative Evaluation

Name

Capella university

NURS-FPX 6016 Quality Improvement of Interprofessional Care

Prof. Name

Date

Quality Improvement Initiative Evaluation

Springer General Hospital implemented a QI activity for Mr. John after the adverse event of his fall. Patient falls are an important safety concern in hospitals, and falls are reportedly one of the leading causes of injury, prolonged hospital stays, and increased mortality. According to Feng et al. (2022), hospitals globally experience approximately 134 million adverse events annually, leading to 2.6 million deaths, many of which are preventable falls.

At Springfield General Hospital, QI initiative focuses on reducing fall-related incidents by adopting evidence-based, ready-to-implement fall prevention protocols such as frequent assessment of risk for falls, staff training, interdisciplinary communication, and combining the use of technology such as bed alarms and Electronic Health Records (EHR) alerts for at-risk patients.The incident involved Mr. John, who reported dizziness but was not reassessed for his fall risk.

NURS FPX 6016 Assessment 2 Quality Improvement Initiative Evaluation

A delayed response to his call light led him to try to walk unassisted and consequently fall, which could have been prevented had better communication, more improved adherence to fall prevention protocols, and timely interventions occurred. In the QI program at Springfield General, falls will be evaluated and mitigated using validated tools, such as the Morse Fall Scale. However, staff members will have to receive ongoing training on preventing falls by nurses and physical therapists, while technology-as in bed alarms and alerts from real-time EHR -will be incorporated to facilitate the early recognition of patients at risk of falls.

However, one of the drawbacks highlighted in this program is the risk of staff alarm fatigue, which can lower the impact of these technologies. Moreover, the hospital was unable to distinctly ascertain the impact of these measures on all areas as different units were not using the new tools introduced to minimize falls in their entirety. These gaps in implementation and overcoming resistance to reporting adverse events for reasons of fear of job security will determine the success of this initiative. With these adjustments, the QI initiative at Springfield General Hospital shall make important cuts in fall-related incidents, improve patient safety, and secure a better environment of care for patients and healthcare providers.

Evaluation of the Success of the Quality Improvement Initiative

The QI initiative was assessed utilizing national benchmarks and outcome measures, such as a fall rate of 3.44 falls per 1,000 patient bed days, with this being one of the benchmark standards set for fall prevention performance (Venema et al., 2019). By comparing its fall rate to this benchmark, Springfield General can determine how effective its fall-prevention protocols are. Other interventions include the application of the Morse Fall Scale as a tool for assessing the individual’s fall risk, staff education and compliance rates, and the support of technology such as bed alarms and Electronic Health Record (EHR) alerts.

These help monitor progress and ensure compliance with safety protocols. Such successful elements of this initiative have been the more consistent use of the Morse Fall Scale, the comprehensive training of staff, and the effective technological integration. These factors have improved the identification of risk factors, which in turn increased response times while reducing fall rates to 2.9 per 1,000 patient bed days.

Several assumptions underlie the success evaluation: that falls are indeed reported accurately, with personnel feeling safe to do so; fall-prevention protocols, including the Morse Fall Scale, are uniformly applied across all units; the technology in place (bed alarms, EHR alerts) is functional and has been integrated into workflows effectively; and that staff received adequate training and are following protocols. Such assumptions are necessary to determine the impact of the QI initiative on the decrease of fall-related incidents and how it upholds the core values of Springfield General, such as safety, patient-centered care, and continuous improvement.

Interprofessional Participants & Actions

Quality improvement (QI) initiatives in the prevention of falls at Springfield General Hospital were significantly enhanced through contributions of an interprofessional team. Nurses, along with physical therapists and physicians, were all very integral in playing their parts within the initiative, giving each profession its own specific perspective. Nurses played a very integral role in identifying at-risk patients and executed fall-prevention protocols such as performing regular fall-risk assessments using the Morse Fall Scale (Baumann et al., 2022).

Physical therapists also contributed through specialized interventions to enhance mobility and strength in the patients, which presumably mitigates falls. Physicians were able to offer insights about medication and overall health conditions that could predispose the patients to fall more than others. Feedback from these healthcare professionals was foundational for frequent meetings and input about the functionality of technologies such as bed alarms and EHR alerts. Together, their efforts helped enhance communication, create uniformity in treatment adherence, and time interventions around falls, resulting in a visible decrease in falls rates (Baumann et al., 2022).

NURS FPX 6016 Assessment 2 Quality Improvement Initiative Evaluation

However, even with these milestones, there existed areas of uncertainty and knowledge gaps that needed to be eliminated. For example, while the technology integration (e.g., bed alarms and EHR alerts) was generally well-received, concerns about alarm fatigue among staff emerged, potentially affecting their responsiveness (Baumann et al., 2022). Nurses reported that the frequency of alarms sometimes led to desensitization, making it harder to prioritize critical alerts. Although the Morse Fall Scale is widely used, some members of the team question whether it accurately accounted for all factors that contribute to fall risk, especially in patients who have complex medical histories.

Additional training regarding the subtleties of fall risk and further data on exactly how specific patient populations respond to specific prevention strategies would have provided a more total conceptualization of the impact of the initiative. Further insights from the staff of all departments, and methods to further heighten technology integration, and the perfect usage and application of assessment tools may have possibly provided even better fall-prevention practices (Baumann et al., 2022).

Additional Recommended Indicators and Protocols

To further develop and expand the results of the fall-prevention QI at Springfield General Hospital, additional indicators and protocols to be considered include: the following Patient-centered outcome measures, for instance patient satisfaction survey targeted specifically towards fall prevention and safety measures would give better feedback on the care of the patients perceived and the hospital’s fall prevention efforts (Dykes et al., 2020). Moreover, surveys on nurse and staff satisfaction about fall-prevention protocols might be able to pinpoint which areas of staff need more support or training.

The hospital should also have post-fall reviews in assessing circumstances surrounding the fall, such as missed opportunities for intervention or gaps in communication and adherence to protocol. Such reviews might yield more specific areas in improvement. In addition, integration of mobility tracking technology such as wearable devices or motion sensors may further help in the real-time monitoring of patients’ movement and enable staff to intervene even before falls occur, especially for those patients who are unlikely to call for help in time (Cooper et al., 2021).

NURS FPX 6016 Assessment 2 Quality Improvement Initiative Evaluation

From technology, predictive analytics through machine learning models analyze patient data, which may include medication history, vital signs, and mobility, improving the identification of patients at risk of falls increases further, tailoring prevention measures according to individual needs (Thapa et al., 2022). However, while the resultant recommendations could considerably enhance the outcome for patients, there are pros and cons:. Adding more outcome measures like post-fall reviews and patient satisfaction surveys would enhance the comprehensiveness of fall-prevention evaluations but add workload on administration and would require additional resources for data collection and analysis.

The integration of the mobility-tracking technology with predictive analytics would allow for real-time, data-driven insights, but this would require significant investment in new technologies and training, and there is a risk of overwhelming the staff with too much data or relying too much on technology over judgment (Raubal et al., 2021). It is therefore important to balance these benefits of the technology and protocol with the implementation and staffing capacity.

Conclusion

In conclusion, the fall-prevention QI initiative at Springfield General Hospital made adequate collective progress by the interprofessional team. While some key achievements were well documented, including improving communication and protocol adherence, rooms still exist for refinement, such as supporting alarm fatigue and enhancing risk assessment tools. The addition of patient-centered measures, mobility tracking, and predictive analytics could further improve outcomes, though these technologies must be carefully integrated to avoid overwhelming staff. Ongoing feedback from staff and patients will be essential in optimizing the initiative. Balancing innovation with practicality will ensure sustained success in reducing fall rates and enhancing patient safety.

References

Baumann, I., Wieber, F., Volken, T., Rüesch, P., & Glässel, A. (2022). Interprofessional collaboration in fall prevention: Insights from a qualitative study. International Journal of Environmental Research and Public Health, 19(17), 10477. https://doi.org/10.3390/ijerph191710477

Cooper, K., Pavlova, A., Greig, L., Swinton, P., Kirkpatrick, P., Mitchelhill, F., Simpson, S., Stephen, A., & Alexander, L. (2021). Health technologies for falls prevention and detection in adult hospital in-patients: A scoping review. JBI Evidence Synthesis, 19(10). https://doi.org/10.11124/JBIES-20-00114 

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), 1–10. https://doi.org/10.1001/jamanetworkopen.2020.25889

NURS FPX 6016 Assessment 2 Quality Improvement Initiative Evaluation

Feng, T., Zhang, X., Tan, L., Su, Y., & Liu, H. (2022). Near-miss organizational learning in nursing within a tertiary hospital: A mixed methods study. BMC Nursing, 21(1). https://doi.org/10.1186/s12912-022-01071-1

Raubal, M., Bucher, D., & Martin, H. (2021). Geosmartness for personalized and sustainable future urban mobility. The Urban Book Series, 59–83. https://doi.org/10.1007/978-981-15-8983-6_6 

Thapa, R., Garikipati, A., Shokouhi, S., Hurtado, M., Barnes, G., Hoffman, J., Calvert, J., Katzmann, L., Mao, Q., & Das, R. (2022). Predicting falls in long-term care facilities: Machine learning study. JMIR Aging, 5(2), e35373. https://doi.org/10.2196/35373 

Venema, D. M., Skinner, A. M., Nailon, R., Conley, D., High, R., & Jones, K. J. (2019). Patient and system factors associated with unassisted and injurious falls in hospitals: An observational study. BMC Geriatrics, 19(1). https://doi.org/10.1186/s12877-019-1368-8

NURS FPX 6016 Assessment 2 Quality Improvement Initiative Evaluation