The field of healthcare and, in particular, nursing practice, provide for the application of various theoretical approaches and concepts aimed at studying the principles of helping people and analyzing potentially successful interventions. These procedures require calculations and making correlations, and in this case, statistics are a valuable method for estimating the required data and their further interpretation.
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Medical personnel providing services to the population often face the task of comparing information about various aspects of work, for instance, the indicators of morbidity and recovery, mortality and survival rates, and other parameters. Statistical analyses are efficient mechanisms for obtaining accurate data based on calculations and affecting not only the quality of care but also such factors as health promotion, leadership, and patient safety.
Relevance of Statistics to Healthcare
Regardless of a particular specialty of qualifications, medical providers often encounter statistics as an indispensable tool for calculations and making conclusions. According to Ocaña-Riola (2016), such areas of work are affected as “research, planning and decision-making,” and each of these stages plays a significant role in creating a sustainable healthcare system (p. 205). In addition, the relevance of this technique is due to the need to obtain the most accurate data since any deviations caused by incorrect calculations are unacceptable if public health issues are considered.
Ocaña-Riola (2016) argues that mistakes made in the process of statistical analyses in the field of healthcare break up the interpretation of the results of certain interventions and, therefore, are the reasons for non-accurate data. This outcome, in turn, can affect public health, which is a severe violation of professional instructions. Therefore, the importance of correct calculations in medical practice is high, and not only the safety of care and treatment are affected but also other significant aspects of activities.
Significance of Statistics to Quality, Safety, Health Promotion, and Leadership
When medical providers resort to statistical analyses as tools for calculating and correlating certain data, this has a positive effect on the quality of work and, therefore, patient outcomes. Hayat, Higgins, Schwartz, and Staggs (2015) give an example of the successful implementation of calculations in nursing activities and note that any research conducted by using this technique is useful and realistic. The quality of the work done is proven due to accurate results, and all subsequent actions are justified.
If healthcare employees use sound and correctly implemented methods of statistical analysis, this influences the safety of the population positively. As Ocakoğlu, Kaya, Can, Atış, and Macunluoğlu (2019) note, patient care in clinical settings depends largely on timely interventions conducted by medical providers. In case a preliminary preparation, for instance, a causal study has been carried out correctly, patients receive a guarantee for qualified assistance and protection against unforeseen consequences of treatment. Therefore, safety is one of the favorable outcomes of utilizing statistics.
Data transmitted to the public about the results of various medical studies are the outcomes of statistical evaluation. According to Hayat et al. (2015), health promotion as one of the aspects of professional practice is largely based on preliminary calculations and reporting. If the population has relevant information on the epidemiology of diseases, the increase of infections, the number of carriers of a specific virus, and other relevant data, this is an additional preventive incentive. As a result, health promotion through statistical evaluation is possible.
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With regard to leadership, the significance of statistics may be expressed in the ability to control specific aspects of activities and making timely decisions regarding the necessary interventions. Hayat et al. (2015) argue that if nursing staff interacts with the administration and takes essential measures to monitor problems and report, this is the key to a stable and professional team. Therefore, the position of a leader largely depends on how well and timely statistical evaluation is performed.
Application of Statistics to Personal Nursing Practice
Since I am a nurse in a hospital, like many other healthcare employees, I face the need to resort to statistical analyses periodically. As a rule, obtaining the necessary data does not carry significant complexity. I collect the information in numbers, for instance, the morbidity rate based on the number of patients and make a proportional ratio. In addition, statistics play an important role in everyday work when it is necessary to determine the number of patients who have been hospitalized or discharged in comparison with the total number of people in the department. In the decision-making process, such calculations may also be useful because the accuracy of the data obtained makes it possible to come to an unequivocal solution. Therefore, I rely on statistics as an effective mechanism for receiving evidence.
With regard to the healthcare sector, statistical analyses are a valuable tool for obtaining the necessary evidence. Moreover, the mechanisms used for correlational calculations and reports contribute to improving the quality of medical care, promoting health, public safety, and maintaining leadership. My experience proves that the daily use of statistics allows me to better navigate the work process and make reasonable decisions based on justified assessment.
Hayat, M. J., Higgins, M., Schwartz, T. A., & Staggs, V. S. (2015). Statistical challenges in nursing education and research: An expert panel consensus. Nurse Educator, 40(1), 21-25. Web.
Ocakoğlu, G., Kaya, M. O., Can, F. E., Atış, S., & Macunluoğlu, A. C. (2019). Nursing professionals’ attitudes toward biostatistics: An international web-based survey. The European Research Journal, 5(2), 326-334. Web.
Ocaña-Riola, R. (2016). The use of statistics in health sciences: Situation analysis and perspective. Statistics in Biosciences, 8(2), 204-219. Web.