Data Analysis in Health: Epistemology

Topic: Healthcare Research
Words: 1965 Pages: 7


Currently, epistemology and ontology are central to many human sciences – from biology, physiology, psychology, and sociology to the philosophy of science and medicine. No science, especially such an integral complex system of cognition as medicine, can successfully solve its research tasks without a philosophical and dialectical understanding of the very essence of human nature and the boundaries of human knowledge. Ontology is understood as a branch of philosophy that studies the fundamental principles of existence (Traoré, 2018). Epistemology is one of the most important sections of philosophy that studies the relationship between man and the world in the process of human cognition.

The ontological analysis of health makes it possible to define it as a universal ontological characteristic of every human being and to attribute it to systems of any nature: physical, biological, technological, social, cultural, or mental. From the ontological point of view, health is the ability of an organism to function optimally. Optimality means that a healthy system has the best possible internal organization and is able to interact with its environment. Since optimality always implies a significant dependence on internal and external conditions, the essential ontological properties of health are relativity and dynamism.

In the ontological sense, the disease is defined as the inability of an organism or system to function optimally and to maintain proper internal organization. Ontological representations allow scientists to introduce a universal classification of diseases, dividing them into systemic, or internal, and adaptive, external (Traoré, 2018). In this tradition, dysfunction and destruction are attributed to systemic diseases and infections, mutations, and catastrophes to adaptive ones. The introduced ontological definitions of health and disease make it possible to perceive them as qualitative, radically different stages of development to show the fundamental differences between healthy and unhealthy people. Ontological concepts allow defining old age as a stage of development in which optimality can be preserved and death as complete and irreversible destruction of internal and external balance.

Epistemology, as a key philosophical discipline, is an important cognitive activity in medicine since the result of cognitive activity in scientific medicine is a certain set of knowledge. To a certain extent, medical knowledge meets such a general criterion of scientific character as the principle of objectivity. However, regarding medicine, it requires some clarifications. Any science is designed to describe an object as it really is, and the results of objective cognition should always be universally valid, that is to say, to be recognized by everyone (Traoré, 2018). While medicine meets the criterion of objectivity of knowledge, it has significant differences from the natural sciences.

As rational knowledge, medicine and social care have their own field of study, their own subject. And such a subject in medicine and social care has always been and remains a person, but not just as an ordinary person, but an individual who needs to be treated or taken care of. In the theory of modern medicine, a person’s illness is understood as a psychosomatic regularity. Hence, the philosophers of medicine declare that the subject of medical science is the psychosomatic patterns of human activity in normal as well as pathological conditions.

Clinical cognition of physicians is organically connected with everyday, heuristic, intuitive and scientific types of cognition. Its specificity is that on the basis of the laws of chemistry, biology, and physiology, as well as the fixed principles of medicine itself, doctors diagnose the disease and determine the ways of treatment. This is a kind of philosophical way of finding and explaining new knowledge gained in medical theory and practice. Medical cognition is multifaceted because it is limitless in understanding the causes and methods of treatment.

Research Methods

The research methods used in health and social care contexts can be subdivided into empirical, theoretical, quotative, and qualitative methods of research. The most commonly used empirical methods are observation and clinical trials or experiments. Observation is a method of empirical cognition that aims to collect, accumulate and describe scientific facts. It supplies the primary material for scientific research. Observation is a systematic, purposeful, and systematic study of reality. Using comparison and measurement, observation provides quotative and qualitative data. In healthcare settings, observation with the help of apparatuses and technical means makes it possible to significantly expand the range of sensory perception (Riley et al., 2019). The benefits of clinical observation lie with the fact that the information received is first-hand and objective. Moreover, it allows pinpointing changes in the condition of an individual, allowing medical and social workers to make adjustments in treatment or care. The drawback is that the human factor in observation is pronounced, and different researchers can construe differently what they see.

Experiment is the most complex and effective method of empirical research that presupposes the study of phenomena through creating new conditions corresponding to the goals of the study. It involves the use of the simplest empirical methods – observation, comparison, and measurement. Observation is a planned and purposeful perception of an object, process, and phenomenon, the results of which are recorded by the researcher. Comparison is one of the main methods of cognition of the surrounding reality. Individual phenomena of a social or medical nature are compared against each other in order to detect distinctive similarities and differences. Based on the comparison, a conclusion of a reasonable or presumptive nature is made about the homogeneity of phenomena, the similarity of their content, and general orientation. Measurement is a research method in which the ratio of one quantity to another is established. This method is common in medicine, where the measurement of all kinds of body indicators serves as a basis for making a clinical decision.

In medicine, experiments often assume the form of clinical trials that are understood as processes of intervention in the human body aimed at changing its physiological or pathological processes for scientific or therapeutic purposes (Riley et al., 2019). The benefits of an experiment are that it allows to test a hypothesis in real life and get reliable feedback on one’s theory. The downside is that the experiments can be dangerous, especially in medical settings, as they lead to unknown results.

Theoretical methods allow systematizing information and include induction, deduction, analysis, and synthesis. Induction is a method of processing information when people move from the general to the particular. The advantage of this method is that it allows pinpointing a particular state or disease based on the common symptoms. The disadvantage is that induction does not allow to consider the symptoms in all their complexity which may result in undervaluation of a patient’s condition.

Deduction is a logical method that allows a transition from the knowledge of a smaller group to a new knowledge of a larger community. In medical and social care settings, the method allows drawing general conclusions on the basis of results of clinical or instrumental studies (Riley et al., 2019). The advantages of the method lie in the fact that it fosters the classification of medical and social states, while the drawback is that often it is difficult to refer a particular case to a definite group.

Analysis and synthesis in their unity give a complete and comprehensive knowledge of reality. The analysis provides knowledge of individual elements, and synthesis, based on the results of analysis, combining these elements, provides knowledge of the object as a whole. In health and social care settings, these methods allow building classifications from particulars as well as infer the probabilities of occurrence of particular social or healthcare issues based on the existing generalizations. The drawbacks of these methods lie in the procedure for obtaining information and the complexity of forming a group classification on the individual judgments of experts.

Research Study Planning

Scientific research in the field of medicine, ranging from international multicenter clinical trials to small scientific projects, requires the same approaches to planning. Being a particular case of scientific research, they are described by a cyclic model that is common to any research process. This model includes the emergence of the need for new knowledge and, accordingly, for new research, problem statement, research planning, and design development. Furthermore, the model comprises the determination of the target population for research and methods of selection of this population, data collection and analysis, and interpretation and presentation of results.

The research question stems from the global hypothesis; the prerequisites that the answer to the research question exists can be found in the materials of the previous research or in scientifically based theories that deserve to be tested. There are three main types of research questions, depending on the answers researchers want to get: description of the phenomenon research question, the difference between the variables research question, and determining the correlation between a variables research question. Once the hypothesis is established, it is essential to develop a project design that, normally, incudes the review of the previous research on the problem and a framework that would be used for hypothesis testing. The target population should be determined, be it a group of volunteers or patients of a particular hospital. The larger this group is, the more representative will be the results of the research. Moreover, the type of data it is necessary to collect and measure for the given research is determined. Once all the necessary data are collected, researchers work out the criteria for the results’ interpretation. The results of practical research are the result of research activities. They are presented in the form of specific qualitative or quantitative indicators put into tables and diagrams. Analyzing the results of practical research, the author compares them with those already scientifically established. The analysis may reveal errors or results that contradict each other; this often happens when different research methods were used. The contradictions will have to be explained, and the conclusion made that allows confirming a hypothesis or refuting it.

Data Analysis in Healthcare Context

Mathematical data analysis is necessary for the interpretation of medical research and is the most important stage in the study of clinical, diagnostic, therapeutic, and preventive measures. In addition, data analysis is one of the fundamental sections of evidence-based medicine. Statistical processing of the results of scientific research in clinical and experimental medicine is necessary to determine the degree of reliability make interpretation and identification of patterns of the studied processes.

Data analysis is the most important tool for the analysis of theoretical, experimental, and clinical observations. Statistical analysis is widely used in diagnostics, allowing creating new scientific hypotheses and solving classification and analytical problems. In medicine and healthcare, various statistical concepts are often used when making decisions on issues such as health assessment, its prognosis, and the choice of prevention and treatment strategies and tactics. Knowledge of statistics is important for understanding and critical evaluation of reports in medical journals, monographs, and reports. Knowledge of data analysis is essential in planning, conducting, and analyzing scientific research in medicine. This is especially relevant for the field of public health and healthcare based on population-based research data. The validity of such studies and their results depend on the application of reasonable statistical principles at all stages.

Mathematical and statistical data analysis, in addition to theoretical knowledge, involves the use of computer programs. Despite the fact that now there are many statistical software packages, such as Statistica, SPSS, MedCalc, SPlus, StatDirect, which allow for fairly complex mathematical calculations, the doctor also needs to understand the logic of applying data analysis (Finkelstein et al., 2020). Without this knowledge, even the availability of a number of available software and hardware does not provide evidence. In most cases, the qualitative analysis of medical data requires the involvement of a specialist with professional training in mathematical statistics. It is in the course of such cooperation that it is possible to count on conducting a deep and correct statistical data analysis.


Finkelstein, J., Zhang, F., Levitin, S. A., & Cappelli, D. (2020). Using big data to promote precision oral health in the context of a learning healthcare system. Journal of Public Health Dentistry, 80, S43-S58.

Traoré, M. K. (2018). Ontology for Healthcare Systems Modeling and Simulation. In Proceedings of the 50th Computer Simulation Conference, pp. 1-12.

Riley, R. D., van der Windt, D., Croft, P., & Moons, K. G. (Eds.). (2019). Prognosis research in healthcare: concepts, methods, and impact. Oxford University Press.

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