Empirical research should be utilized in the field of health care to provide excellent and safe care to patients. In the area of medicine and its related sources, such as health care policies, there are many different approaches to gathering information to utilize to improve the art of health care. Some researchers prefer to use causal models versus empirical methods. The testing that is utilized for such research depends on those completing the investigation and what is being studied. Causal models examine what happens to a dependent variable while an independent variable changes over time (McLaughlin). These types of studies are also known as cause-and-effect. Conversely, empirical studies require scientific research to prove the hypothesis (Shmueli). “While explanatory power provides information about the strength of an underlying causal relationship, it does not imply its predictive power” (Shmueli). Therefore, empirical data is much more important to medical research.
Causal models will examine two variables that seem to be related to one another. Researchers will then hypothesize the relationship between the two variables. The model will then prove the correlation between the two variables. However, it must be known that correlation does not imply causation, meaning although they may be related, the relation may be coincidence. If the relationship is because of a coincidence, it is not a true relationship in which when A occurs, B will always also occur. As a result of the correlation, a theory is constructed. On the other hand, empirical models prove that when one event occurs, another event also always occurs. The two variables are related because one causes the other. This is proved through the use of scientific research and the scientific method. A hypothesis is made, an experimentation occurs, and an observation is noted.
In the realm of science and health care, it is important to utilize evidence-based practice. In this field, health care professionals care for sick individuals in an attempt to alleviate their suffering and disease. Lives are in the balance and only scientifically proven treatments should be used. Thus, empirical research should be utilized in the field of health care, whether it be at the bedside or creating policies to keep patients safe. “Health care experts widely view the development of evidence-based practice as essential to improving health care quality and efficiency” (Bernstein, Chollet, & Peterson). Empirical evidence allows researchers and health care practitioners to utilize resources accordingly, drive down costs of health care, and provide the best care possible for the situation (Bernstein, Chollet, & Peterson).
In the world of healthcare, new technologies are being developed each day. These new developments can be costly and take years to be put into practice. Evidence-based practice examines the new technologies and how they affect their test groups before they are utilized in practice. It must be proved that these new treatments do what is intended and do not put the directed population at risk. Furthermore, when problems and complications arise in healthcare, policy makers examine the processes and do their own research to improve the treatments. Before the policies can be set in stone and instituted, they must be examined to be sure they are effectively remedying the situation and not causing any harm.
Empirical evidence presented as evidence-based practice improves the quality of healthcare for practitioners and those they serve. Empirical evidence proves that what is being done is treating the problem with low risk. In healthcare, the goal is to improve patient health and safety. In order to do so, we must be following policies and procedures that do so. The only way to know what we are doing is correct is to test the policies and procedures with the scientific method.
References
Bernstein, J., Chollet, D., & Peterson, S. (2010). Basing Health Care on Empirical Evidence.
Mathematica Policy Research, (3). Retrieved February 19, 2016.
McLaughlin, C. P. (2008). Health policy analysis: An interdisciplinary approach (2nd ed.).
Sudbury, MA: Jones and Bartlett.
Shmueli, G. (2010). To Explain or to Predict? Statistical Science Statist. Sci., 25(3), 289-310.
Retrieved February 18, 2016.
