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In general, the use and organization of statistical data are used in medical practice with a wide range of applications. The main task is to establish specific generalized information that could help in medical or professional practice and improve the work of medical personnel. Moreover, the results of statistical studies can be used to disclose and inform the general public in order to increase the awareness of the population. The types of statistical analysis practices are divided into traditional parametric and non-parametric. The first and most common type of analysis uses statistical data and computational methods in order to guess the repeatability and logic of the development of a calculated parameter (Gray et al., 2021). If the results of traditional computational analysis turn out to be insufficiently accurate or produce an insufficient amount of new information, they are considered invalid. In this case, testing and statistics collection models are needed that are nonparametric and offer other ways of calculating and organizing information.
In the first described experimental case, the approach of the assistants of a group of patients with intellectual disabilities is considered. The problem of helping mentally difficult patients in medical practice has not been considered in sufficient detail. Therefore, the study aims to show the breadth of necessary practical skills of nurses and proxies that are often ignored by the scientific community (Fisher et al., 2010). In order to demonstrate all the complexity of decision-making in various special cases, an unconventional computational approach was required. In the study, the primary need is not only to provide a slice of information that affects the entire structural complexity of decision-making, but also to organize it in detail. In this case, the researchers had too many hypothetical scenarios to take into account, which made it necessary to resort to an experimental nonparametric model. One such model used was conjoint analysis, in which the results were organized in ranking order to avoid, if possible, two-way comparisons. Such a statistical model is a frequent nonparametric alternative that is admittedly hardly less successful than the traditional one and useful in cases of excess scenarios.
Another case describes a two-phase experimental implementation of changes in medical practice to observe the changes made at a statistical level (Tjia et al., 2010). This experiment was carried out in order to prove the possibility of reducing the prescription of high-risk medications. The Delphi technique was used, implying multilevel and anonymity, which ensured the identification of significant differences in the quality of service in different nursing houses. The researchers used employee guidelines for experimentally documented professional development, but like the first, they focused on the use of experimental scenarios. Thus, one can say that computational models like conjoint analysis using simulation scenarios can be extremely useful in obtaining data related to medical decision-making. This field of research, in addition to being insufficiently studied in some areas of medical practice, requires an experimental approach. The excessive number of hypothetical scenarios does not allow organizing and calculating information in the traditional way.
Therefore, either computational models that organize research results in order of ranking, or specific experiments in focus groups, are required. Traditional computational models do not take into account unforeseen scenarios, while the practice of decision-making is extremely ramified (Varabyova & Schreyögg, 2018). Therefore, an experimental and hypothetical approach is applicable to this area of research. In the experimental approach, while observing the measures of correspondence and multilevelness, it becomes possible to directly observe and register the changes made. The hypothetical approach takes into account the unforeseen development of events and thus is no less valuable for non-traditional medical analysis.
References
Fisher, K., Orkin, F., & Frazer, C. (2010). Utilizing conjoint analysis to explicate health care decision making by emergency department nurses: A feasibility study. Applied Nursing Research, 23(1), 30–35. doi:10.1016/j.apnr.2008.03.004
Gray, J.R., Grove, S.K., & Sutherland, S. (2021). The practice of nursing research: Appraisal, synthesis, and generation of evidence (9th ed.). Saunders Elsevier.
Tjia, J., Field, T., Garber, L., Raebel, M., Donovan, J., Kanaan, A., & Gurwitz, J. (2010). Development and pilot testing of guidelines to monitor high-risk medications in the ambulatory setting. Clinical Medicine & Research, 8(3-4), 197–197. doi:10.3121/cmr.2010.943.c-b3-03
Varabyova, E., & Schreyögg, J. (2018). Integrating quality into the nonparametric analysis of efficiency: A simulation comparison of popular methods. Annals of Operations Research, 261, 365–392.
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