Exploring Research Methods in Healthcare: Effectively Implementing Research

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Table of Contents

Qualitative and Quantitative Research Paradigms
Parametric and Non-Parametric Statistics
Role of Clinical Terminologies
Potential Benefits of Healthcare Informatics

Qualitative and Quantitative Research Paradigms

Differences

Understanding the principal difference between qualitative and quantitative research methods is paramount to implementing research effectively in healthcare and producing the results that will allow improving the quality of respective health management strategies. As the names of the specified paradigms suggest, the qualitative research focuses on exploring the qualitative aspects of variables under analysis, whereas the quantitative method considers the quantitative correlations between them. However, examining the issues further, one will spot more profound and detailed differences between the two research methods.

Namely, the presentation of objective truths and the concept of reality varies depending on whether the qualitative or quantitative paradigm is applied. By definition, the introduction of the qualitative research method implies adhering to the established truths that are deemed as objective, as well as the reality that is described as singular and quite well-defined. In turn, the quantitative approach negates the very presence of objective truths and, instead, considers multiple subjective ones as the environment in which a hypothesis is tested. As a result, the concept of objective reality as a setting in which the research takes place is questioned. The described frameworks define the application of respective qualitative or quantitative research methods.

Qualitative Approaches

Phenomenology. Phenomenology is typically used in the qualitative research paradigm as the means of exploring a specific phenomenon and examine its nature, history, key characteristics, and relevant properties.

Grounded theory. The grounded theory approach to the qualitative research method implies considering the existing evidence in order to build a viable theory that explains a particular phenomenon or a string of events.

Discourse analysis. The introduction of the discourse analysis into the qualitative research method suggests examining specific textual data to locate its key properties or identify the essential similarities or differences between several texts.

Thematic analysis. Similarly to the discourse analysis, the thematic analysis suggests working with a piece of text. However, as the name suggests, the thematic analysis concern itself with the search for the relevant themes in the text at hand.

Quantitative Approaches

Experiment. One of the basic approaches to the quantitative research, the experiment implies creating an environment in which a certain hypothesis established in research can be tested under controlled circumstances. In the healthcare context, a randomized clinical trial can be deemed as a common example of an experiment in research.

Quasi-experiment. The concept of the quasi-experiment is very similar to that one of an experiment. However, in a quasi-experiment, the participants are not selected randomly for the control group or the experimental group. Therefore, the extent of control is higher along with the rate of bias.

Field experiment. A field experiment is typically conducted in the context of a real-life setting, which is why its nature is unobtrusive compared to an experiment. moreover, moving from laboratory settings to the hospital environment allows applying the principles of evidence-based practice.

Case study. Implying an investigation of changes observed across a particular group, a case study allows considering the efficacy of health management steps in an EBP setting by using observations. A case study implies a certain degree of intensively.

Parametric and Non-Parametric Statistics

The application of a statistical analysis allows exploring the effects of a particular healthcare intervention more accurately and determine the correctness of an established hypothesis with extraordinarily high precision. Therefore, in quantitative research, the choice of a sample population is critical to the outcomes of the assessment and their implications for the target population. For this reason, considering the differences between parametric and on-parametric statistics as the methods of gauging the distribution between the research samples has huge importance.

By definition, parametric statistics allows making conclusions about the parametric specifics of the populations used to draw a corresponding sample for the research. In turn, non-parametric statistics strategies do not provide such an opportunity for researchers, which, arguably, devalues the accuracy of research results. The described difference in the approach toward the sampling process can be considered the essence of the differences between parametric and non-parametric statistics in quantitative research.

However, apart from the nuance described above, there are other aspects of parametric and non-parametric tests that allow one to differentiate between the two. Specifically, non-parametric tests allow for greater flexibility for researchers since they offer a chance at considering both normally distributed data and the one that has been distributed non-normally. As a result, the testing of normality as one of the key parameters needed for conducting the qualitative research is not required when applying non-parametric tests.

However, the difference outlined above does not suggest that bon-parametric tests are inherently superior to the parametric ones. Instead, they need to be recognized for the unique options that they provide in specific circumstances. In turn, parametric tests appear to be of greater use in the contexts where one needs to consider other variables apart from the significance test results.

Moreover, parametric tests provide greater options for modelling due to the characteristic described above. As a result, opportunities for representing the target environment and patients with their needs increase in number, creating a more friendly research setting. Therefore, the application of parametric tests allows for greater precision in modeling compared to non-parametric ones.

The differences between the two types of tests are not restricted to the specified issue. In addition to the points outlined above, the extent of statistical power that the specified types of tests have needs to be addressed. As a rule, parametric tests are believed to have greater statistical power compared to non-parametric ones and, therefore, will help to locate a greater range of potential differences between the clusters of data.

Considering the examples of pparametric tests, one may suggest the following ones: one-sample test, two-sample test, one-way ANOVA, and factorial DOE. As for non-parametric ones, one-sample Wilcoxon, the Mann-Whitney test, Mood’s median test, and Friedman test should be named.

Overall, parametric and non-parametric statistical tests share a common goal of locating the presence of variance across a specific group, yet they imply selecting slightly different ways of determining the findings. As a result, parametric and non-parametric statistics needs to be applied to the corresponding contexts that allow each method to shine and help to deliver the most accurate outcomes possible.

Role of Clinical Terminologies

Applying healthcare-specific language in research regarding respective health-related topics plays a paramount role in conveying key information accurately, providing a brief summary of key data, and improving cross-disciplinary collaboration by using the language understood by all parties involved. With the integration of innovative technology, opportunities for improving the use of clinical terminology and managing data even more effectively have been produced.

The clinical technologies in question also reduce the probability of making a medical error in the clinical environment or an oversight in health research to a noticeable degree, thus making healthcare informatics ore effective. Therefore, the incorporation of the specified technologies into the identified contexts is highly advisable. In addition, the introduction of clinical terminologies contributes to the simplification of working with electronic databases, which a lot of nurses and healthcare practitioners still find quite complicate, especially in the interdisciplinary context. While the presence of multiple data sources and the need to arrange each according to a specific property may seem convoluted to healthcare team members, the application of clinical terminologies will allow making working with an electronic database significantly simpler when viewed from the perspective of healthcare informatics. The resulting increase in the opportunities for data reuse will allow improving the efficacy and speed of care delivery to a massive extent. Moreover, the improvement in data management will entail better clinical decision-making and the relevant support. In addition, the efficacy of cost monitoring can also be improved with the inclusion of the clinical terminology since it will allow allocating the existing resources with greater efficacy.

Finally, the issue of patient safety should be addressed as one of the core areas that the inclusion of clinical terminologies into the healthcare setting will affect. With the inclusion of tools such as SnoMed CT, healthcare providers will be able to reduce the rate of clinical errors and make the key tasks such a diagnosing, patient assessment, and the task of administering treatment, much more effective. As a result, the management of external factors in healthcare informatics and the subsequent rise in the levels of patient safety will rise in the clinical context, which is another important argument in favor of promoting clinical terminologies in the target setting.

Remarkably, even with the opportunities hat clinical terminology offers to healthcare experts, none of the current databases contains the entire range of information that the healthcare context encompasses. Nonetheless, the role of the specified tools in improving the quality of administered care is central to patients’ faster recovery since it also contributes to the creation of homogenous guidelines and standards of care to be implemented in all clinical contexts.

By deploying clinical terminologies into the context of healthcare informatics, one can advance the quality and speed of data management, increase the efficacy of healthcare services, and encourage the development of solutions to the current public health problems. Due to the options for arranging, using, keeping, and transferring data that the specified technological innovations provide, one can improve the efficacy of EBP research and health services in the clinical context substantially.

Potential Benefits of Healthcare Informatics

Healthcare informatics is one of the fields that have a massive amount of potential for changing the current healthcare setting positively. Therefore, the opportunities that it offers are to be scrutinized very carefully so that they could be implemented accordingly and that patient recovery rates could be improved tremendously. Nonetheless, healthcare informatics still contains a range of grey areas that are yet to be explored. Presently, four potential benefits of healthcare informatics can be identified as the main pointers toward the direction in which the subject matter needs to be developed. Specifically, the issues such as data mining and machine learning, data integration based on the principles of ontology, the promotion and research of consumer health, and text mining in healthcare.

Data mining and machine learning as the area with the greatest value an potential for improving the quality of healthcare and the efficacy of information management is the first option to consider when scrutinizing the benefit that healthcare informatics provides. Collecting data is a tedious process that is often reduced to its bare minimum due to nurse shortage in the clinical setting and the need to address each patient’s concern individually. Therefore, the specified task could use automation, hence the need to explore the described area in greater depth.

Ontological data integration is another benefit that healthcare informatics has introduced into the field of health, management. Specifically, due to the presence of nuances in the semantic characteristics of textual data, the process of automation may be hampered significantly. In turn, the introduction of healthcare informatics and especially the tools for encouraging machine learning, will allow resolving the specified issue. The identified problem represents one of the major problems for the IA development, which is why it should be seen as the area of health informatics development that holds the greatest potential for future management of patient information.

The research of consumer health is another opportunity that healthcare informatics provides. Due to the improved management of data, healthcare informatics will allow distilling key trends in consumer behavior, thus defining the decisions that buyers make when purchasing healthcare-related products. Thus, informatics will advance the specified area to a great degree.

Finally, the concept of text mining deserves attention as one of the prospective areas for healthcare informatics development. When considering the texts that are expected to provide critical data for the further management of patients’ health issues, a nurse typically faces difficulties in translating the obtained information into the format that machines can recognize and digitalize for further access of data for all healthcare members. Namely, the language format used by nurses in the clinical setting to extract the needed data can be characterized by high rates of fluidity, richness, and flexibility. The described properties are likely to be lost when transferring the spoken language to the digital one, which is why further studies on how the processing question can be improved and automated are needed.

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