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Posted: April 3rd, 2022
Application of Data
The healthcare scenario involves the poor allocation of nurses during shifts that have more patients that require to be attended to. The poor allocation can lead to missed treatment due to the few nurses who are overwhelmed by the high number of patients who need treatment (Hayes, Douglas & Bonner, 2015). It can also lead to a high cost of labor when the skills of nurses are not optimally utilized. Data can be applied to identify the optimum number of nurses who should be allocated during specific shifts (Kouatly, Nassar, Nizam & Badr, 2018). The accurate allocation would boost patient satisfaction, reduce medical malpractice and eliminate missed treatment.
The data that could be used include the number of patients who visit the healthcare facility for treatment. The data can be broken down into the outpatients who visit during morning hours, lunch, evening and during the night (Hayes, Douglas & Bonner, 2015). There is also a need to consider the variations during weekends and holidays. It also comprises the number of patients in the different wards and the kind of medical attention they require. It is also necessary to ensure the hospital considers the patients who visit the hospital periodically for checkups (Hall, Johnson, Watt, Tsipa & O’Connor, 2016). Emergencies can also be catered for. The data will be essential in allocating the appropriate number of nurses for each shift.
The data can be collected and accessed using various strategies. The first strategy is to check the past records of the number of patients who are treated daily. The data from the hospital on the number of patients in the wards and the total capacity is important (Hall, Johnson, Watt, Tsipa & O’Connor, 2016). The number of patients who require regular monitoring is also necessary. The data on the satisfaction of patients can be measured against the number of nurses allocated during specific shifts. It will be used to assess the effectiveness of the nurses in handling a specific number of patients (Hall, Johnson, Watt, Tsipa & O’Connor, 2016). The top management is instrumental in accessing the data from the systems, past records, and future projections. Informaticists will also be crucial in accessing the necessary information to make informed decisions.
The knowledge that will be derived from the data is the various gaps that have been experienced in the past. It will show the level of efficiency of a specific number of patients. The data will be used to show the medical errors that have occurred due to burnouts (Kouatly, Nassar, Nizam & Badr, 2018). It will point towards the need to create friendly shifts for the nurses to promote efficiency in their work. The investigation will show the cost implications of allocating specific healthcare providers (Hayes, Douglas & Bonner, 2015). Therefore, the knowledge will be used to recommend the necessary changes that should be adopted to promote the efficiency of nurses, increase the quality of care and reduce burnouts.
The nurses would apply clinical reasoning and judgment to develop appropriate measures to be taken to allocate the nurses for different shifts. It would be used to know the expertise required in different sections and the number of nurses who should be allocated (Swiger, Vance & Patrician, 2016). The reasoning would also inform the specific days a healthcare facility should be cautious to allocate more healthcare specialists to cater to an outbreak or emergencies (Swiger, Vance & Patrician, 2016). For example, over the weekend, hospitals are likely to flock with people who need medical attention. The reason is that most of them are not working and road accidents may increase over weekends. The information is thus necessary to make appropriate clinical reasoning.
References
Hall, L. H., Johnson, J., Watt, I., Tsipa, A., & O’Connor, D. B. (2016). Healthcare staff wellbeing, burnout, and patient safety: a systematic review. PloS one, 11(7), e0159015.
Hayes, B., Douglas, C., & Bonner, A. (2015). Work environment, job satisfaction, stress and burnout among hemodialysis nurses. Journal of nursing management, 23(5), 588-598.
Kouatly, I. A., Nassar, N., Nizam, M., & Badr, L. K. (2018). Evidence on Nurse staffing ratios and patient outcomes in a low‐income country: Implications for future research and practice. Worldviews on Evidence‐Based Nursing, 15(5), 353-360.
Swiger, P. A., Vance, D. E., & Patrician, P. A. (2016). Nursing workload in the acute-care setting: A concept analysis of nursing workload. Nursing Outlook, 64(3), 244-254.
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