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As short-staffed hospitals are becoming a common issue around the world, methods to improve this situation must be reviewed. One research method that includes mathematical modeling and analysis to yield optimal results is the operations research method (Saville et al., 2019, para. 3). In order to improve nursing staff shortage, a system of employment should be adapted, which would reorganize staff based on the number of patients and other factors. Hence, operations research is applicable in the process of creating a particular employment schedule.
To create a practical schedule that would indeed resolve staff shortage, several techniques can be used. In this case, the methodology includes optimization, simulation, queuing theory, and forecasting (Saville et al., 2019, para. 19). Optimization aids in calculating staff numbers and arranging them in ways to decrease overall costs and, at the same time, replace nurses with other staff members to ensure the patients’ needs are being met (Saville et al., 2019, para. 20). Simulation is applicable in regards to acquiring the range of nurse demand daily, while forecasting performs similar functions but for future reference (Saville et al., 2019, para. 20). Unlike most traditional methods of dealing with staff shortage, the queuing theory additionally considers time-related factors (for patient care), providing data that corresponds to realistic nursing shifts (Saville et al., 2019, para. 22). In this way, operations research methodology offers medical institutions accurate data regarding employment, deployment, scheduling, and nurse demands on specific days.
Furthermore, a comprehensive study of the specific ways the mentioned methods are applied will highlight their practicability. One research study demonstrated the use of mathematical modeling to systemize nurse shifts depending on the emergency. The model considered factors such as length of shift and meal coverage, patient arrival (time), and the number of patients by an hour (Svirsko et al., 2019, para. 2). The model’s product is a schedule that allows staff to self-schedule while reducing the former time needed to arrange such timetables and without risking staff shortage for emergencies (Svirsko et al., 2019, para. 6). Although traditional methods presumably seem more applicable to realistic situations in the hospital, they often fail to consider several factors simultaneously. Through mathematical modeling, researchers are able to create 8-and 12-hour timetables, which include meal breaks and nurse preferences (Svirsko et al., 2019, para. 16). More specifically, the mathematical model leads to calculating a linear program (equations). In this way, the program calculated the number of the minimal shift needed to have enough nurses on duty, including the 8- and 12-hour shifts (Svirsko et al., 2019, para. 17). The yielded results became the basis for the final daily schedule.
It is essential to discuss how introducing the operations research methodology can improve the current nursing staff shortage, with reference to the mentioned studies. As the enhanced schedule allows for self-allocation, it increases staff satisfaction, and nurses are more likely to work their arranged shift, decreasing staff shortage rates. Furthermore, as the timetables are programmed with set meal times and breaks, the overall working conditions improve significantly.
Of course, the proposed operations research methodology also has limitations. In this way, the mathematical model requires additional shift hours in most hospitals, which significantly increases costs annually (Svirsko et al., 2019, para. 25). Additionally, it might not work as effectively in cases of extreme staff shortage and requires nurses to be able to use it correctly and make changes when necessary (Svirsko et al., 2019, para. 25). Therefore, the reviewed models must be used only after evaluating the possible risks in cases of staff shortage. In the case that a hospital decides to implement certain techniques into their working schedule, the staff must be appropriately trained to ensure increased productivity.
References
Saville, C. E., Griffiths, P., Ball, J. E., & Monks, T. (2019). How many nurses do we need? A review and discussion of operational research techniques applied to nurse staffing. International Journal of Nursing Studies 97, 7-13. doi:10.1016/j.ijnurstu.2019.04.01
Svirsko, A. C., Norman, B. A., Rausch, D., & Woodring, J. (2019). Using Mathematical Modeling to Improve the Emergency Department Nurse-Scheduling Process. Journal of Emergency Nursing 45(4), 425-432. doi:10.1016/j.jen.2019.01.013
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