Nursing is a core initiative in the healthcare service spectrum, and it is appropriate to have an appropriate number of nurses across the continuum. There must be an appropriate mix of education, experience, and skill that meets the normal working condition of the care in health facilities (Bianchi et al., 2018). Safe nurse staffing remains a critical issue in the wellbeing of patients and every community needs to have all logical minimum thresholds to assure them of excellent services (Jin et al., 2020). Insufficient nurse staffing levels make the medical field have uncertainties because their service provision ratios are compromised. The workload in the healthcare sector needs to be rationalized with the number of patients that a hospital serves to eradicate poor service delivery.
The consensus of organizational culture can be shaped by attenuating various programs within the identified setup. The leadership structure can be perceived as the core joint of communication hence the need to decentralize the system. Decentralization will enable nurses to have their ideologies heard, and a culture of teamwork will be established. Culture change in the healthcare system is essential in ensuring effectiveness in the regulation of perception hence increasing staff efficiency in handling patients.
Readiness Tool and Assessment
Medical sciences and diagnostic methods must be done with utmost precision. Therefore, the organization should ensure specialized equipment and advanced technologies when handling patients. The readiness tool is influenced by the power an organization holds in dealing with emergencies. The facility must have a swift mode of operations, and the synchronization of a decentralized leadership structure will enable them to provide the best services for patients. The readiness tool is the organizational assessment reports that should be analyzed periodically to note the sequence of every department within the hospital. Using the organizational readiness to change assessment (ORCA), the following are three major scales that will guide the research.
- The nursing sector will gauge on the strength of the evidence for the proposed change and innovations.
- The quality of the organizational context to support the changes proposed herein.
- Organizational capacity to facilitate the change.
Recommended Healthcare Process and System
The healthcare process must break service delivery barriers, such as poor timekeeping and having a sustainable number of staff members. The stakeholders should ensure the medical vicinity capitalizes on its strengths and neutralizes the weaknesses. The readiness checklist will be synthesized with the facility’s resources and have a comprehensive analysis of fundamental things, yet they are lacking in the hospital.
Information and Communication Technologies
Every department in the hospital entails passing information from one party to the other. Making the system efficiency can help the practitioners serve clients with the care it deserves. Having advanced software that improves expert communication will instil patient-centred healthcare. The stakeholders should improve communication between hospitals to facilitate transfers and privatization of health records.
In acute care Hospitals (P), does the level of Staffing of Nurses (I), based on mandatory policies securing good nurse-to-patient ratios (1:5) (C)? (O) Improved patient length of stay (T) 1 year.
Safe nurse staffing is a significant issue for patient safety and the quality of care in hospitals, communities, and all settings in which care is provided. ICN (2018) reported that inadequate or insufficient nurse staffing levels increase the risk of compromised care, adverse events for patients, inferior clinical outcomes, and inpatient death in hospitals, and poorer patient expertise of care. Having inadequate or inappropriate nursing staff to meet patient needs also results in unsustainable workloads and negatively affects the health and wellbeing of staff. Research suggests that investing in safe, effective, and needs-based nurse staffing levels can be cost-effective, promoting the improvement of and preventing deterioration in patients’ health, thereby reducing the duration and intensity of healthcare interventions (Molle & Allegra, 2021). Another study was conducted by Butler et al. (2019) about Hospital nurse-staffing models and patient and staff-related results. The study was to explore the effect of Hospital nurse staffing models on patient and staff-related outcomes in the hospital setting, specifically to identify which staffing models are associated with the following:
- Better results for patient care
- Better staff-related outcomes.
- The impact of staffing model on cost outcomes.
Butler et al. (2019) reported that the study suggested interventions related to hospital nurse staffing models to improve patient outcomes, clearly adding specialist nursing and specialist support roles into the nursing workforce. On the other hand, Shin et al. (2018) reported on their study findings demonstrate that a higher nurse-to-patient ratio is related to adverse nurse outcomes. In contrast, Blume et al. (2021) argued strong evidence for a significant association between nurse staffing levels.
In the chosen study, I chose to analyze the population of 432 nurses and 58,000 patients from a 320-bed tertiary care center famous for inpatient and outpatient services. The current institution would be a 344-bed care center that has over 1600 employees that serve up to 100,000 patients in a year. The patient randomization scale includes the inpatient and outpatient. The study area would focus on the number of staff nurses that would serve the patients admitted in the medical surgery department and the CCU unit.
The interventions to be implemented
There is a need to know the nurse-to-patient ratio before taking the implementation plan. Once the ratio has been established, the simulation analytics will ascertain that the patients’ outcomes will be affected when nurses go down. The data collection is based on the cumulative sequence of the patient outcomes. After that, the realism of getting the minimum threshold sequence of the nurse will establish the optimum ratio of the nursing staff based on the annual workload (Dietermann et al., 2021). The first intervention is to gauge the staff nurses to patient ratio to be 1:5.
Comparison of the baseline data sources (datasets and EMR), methods, and measures
The first analytical data is the daily unit assignment sheet to gauge against the rationed 1:5 ratio. The daily assignment sheets will provide me with the best analogy on handling the rationality based on service delivery. The mean average sequence of the nurses will have a basis for getting a clear picture of modes of services based on the day of the week (Winter et al., 2021). For instance, sometimes, the nurses get slower towards the weekend compared to Mondays. The EMR will be used in data collection to negative patient outcomes within the nursing care unit, such as injuries, patient falls, CLABSIs, HAIs prevalence rates, and mortality.
The outcome of the data collected will be equated to the nurse and patient ratio. After that, the data will be analyzed to see a link between the nurse and patient ratio. Equally, the ratios will clarify patient outcomes based on the day of service delivery. The use of computer software such as the SPSS will compare with a statistical analysis that would enable us to compute numerical data (Winter et al., 2021). The SPSS will enable the comparison matrix to co-relate with the nurse ratios and patient outcomes.
The outcome expects to ensure that the data produced shows there is better handling when the nurses have a reduced workload in terms of providing services to the patients. Subsequently, there would be reduced HAIs and mortality rates due to timely service delivery. There would be a correlation between the nurse staff and the positivity rate to patients’ outcomes. When the nurse-to-patient rates lower (x), the HAIs, falls, injury, CLABSIs, and the mortality rates reduce (y). The probable sequence when the nurses reduce would be a negative occurrence to the patient outcomes. For instance, when there is a high nurse to patient ratio (x), the resultant occurrence is increased injuries, HAIs, falls, CLABSIs, and mortality rates (y). The timeframe of the analysis is one year.
The organizational culture must ensure longevity and satisfaction of their ethical orientation in the healthcare sector. The leadership structure should be very sensitive in decision-making because having normal operations in the hospital will result in fatalities. The hospital sector needs an elaborate team that can make delicate decisions that are fundamental to the patient’s wellbeing. The best readiness tool ensures a balanced ratio of 1:5 between nurses and patients, respectively. When the ratio is balanced, there is enough timeline and reduced workload to the nurses, giving their best intervention during service delivery. Based on cost-effectiveness, there is a reduced cost in service delivery because when the nurses are enough, most hospital services will be reduced. Equally, when the services are delivered in time, there is the likeliness that most infections or diseases will be handled while still mild. The readiness of an organization is gauged on the preparedness of the leadership. Leaders can adjust healthcare interventions to suit their patients’ needs.
The hospital must have centralized information and communication technologies to link up nurses within the vicinity within and outside the hospital. Communication must be effective to ensure the facility is ready to handle an incoming emergency and professionals can make decisions easily. The stakeholders can also make inquiries about their insecurities or provide an insight that is delicate but fundamental.
Bianchi, M., Bagnasco, A., Bressan, V., Barisone, M., Timmins, F., & Rossi, S. et al. (2018). A review of the role of nurse leadership in promoting and sustaining evidence-based practice. Journal of Nursing Management, 26(8), 918-932. Web.
Dietermann, K., Winter, V., Schneider, U., & Schreyögg, J. (2021). The impact of nurse staffing levels on nursing-sensitive patient outcomes: a multilevel regression approach. The European Journal of Health Economics, 22(5), 833-846. Web.
Jin, Y., Huang, Q., Wang, Y., Zeng, X., Luo, L., & Pan, Z. et al. (2020). Perceived infection transmission routes, infection control practices, psychosocial changes, and management of COVID-19 infected healthcare workers in a tertiary acute care hospital in Wuhan: a cross-sectional survey. Military Medical Research, 7(1). Web.
Molle, E., & Allegra, M. (2021). Nurses’ Perceptions of the Buddy Staffing Model. Nurse Leader, 19(6), 625-629. Web.
Winter, S., Bartel, A., Cordova, P., Needleman, J., Schmitt, S., Stone, P., & Phibbs, C. (2021). The effect of data aggregation on estimations of nurse staffing and patient outcomes. Health Services Research, 56(6), 1262-1270. Web.