Assignment Important of Statistics on Health Sciences Article Analysis
Today, statistics is applied in all sectors to show trends in the markets and to enhance the planning processes for the success of an organization. Statistics involves the analysis of collected data to inform the decision-making processes. Healthcare organizations mostly use statistics to show the trends in the emergence and spread of diseases, plan for the medical operation processes, and control the treatment processes, among other activities meant to increase efficiency in the healthcare system. There is always the need for healthcare systems to enhance care delivery and ensure effective patient outcomes. As a result, healthcare institutions often engage in numerous research processes. Besides, in the modern clinical setups, there is the application of evidence-based practices to enhance, diagnosis, treatments, and overall patient care.
Importance of Statistical Knowledge in Healthcare
The knowledge of statistics is used in the modeling processes to formulate the best treatment approaches that are essential for specific types of patients. Statisticians often rely on clinical data to predict and inform any possible variation in the treatment and patient management (Jahan et al., 2016). For instance, whenever there is a disease outbreak, as in the coronavirus case, clinical data collected can be used to determine the possible future trends and the number of resources that may be required to manage the situation. Statisticians may also model a system or program that may show the best precautions to take to reduce the spread of the diseases through the application of data collected. Different processes in the health care system rely on statistical data. For example, when writing a treatment or medication report, healthcare professionals often use the data that have been collected over a given period (Milic et al., 2016). As a result, healthcare professionals need to have basic or advanced knowledge in statistics to create meaningful reports and make statistically significant conclusions. The modern treatment models rely on statistical data. For instance, the development of artificial intelligence that can facilitate the medication process depends on the data and statistical trends in the healthcare system.
Before a research process is considered medically significant, it must always pass through the statistical tests to ensure that it is statistically significant. All the above processes require significant statistical knowledge to help in the accurate analysis of every scenario from study design, data collection, data analysis, interpretation, and writing of a comprehensive medical report. Most of the medical research companies such as Center for Disease Control (CDC) and World Health Organization (WHO) depend on the statistical knowledge when it comes to drug trials, research on various diseases and study of the best medical interventions mechanisms that can lead to effective patient outcomes (Milic et al., 2016). As a result, there are always teams of epidemiologists and biostatisticians who have advanced knowledge in statistics, nature of diseases, and other healthcare activities. With the use of data in the treatment process, there is always the need for healthcare workers to show competency in healthcare informatics.
The application of statistics in the healthcare system is significant to quality improvement. To ensure efficiency in the healthcare system, there is always the need for healthcare professionals in collaboration with the researcher to carry out research to determine areas that require improvements. The use of data can, therefore, informs the ideal solution to the problems facing healthcare institutions. Therefore, the result of the clinical analysis can be used to improve the quality of care and the general treatment processes. Research processes may inform the safety measures, health promotion, and best leadership approaches that can best improve general care.
With the emergence of diseases and different complications in the healthcare settings, the provision of healthcare services often turn
to be a challenge; as a result, there is always the need for healthcare professionals to identify the right strategies that can be used to improve the treatment outcome and the general safety. Therefore, statistical knowledge is required to undertake research using the clinical data available to determine the best strategies that can be used to deal with the increasing problems.
In my organization, given the introduction of technology and numerous research processes, I will mostly utilize statistics in almost all the activities, from data collection, clinical analysis, medical research, clinical reporting. The statistical knowledge will also be critical in the development of treatment and care models in line with the needs of the patients. In healthcare settings, the clinical data can be obtained from the databases. Also, in cases where there is the need to study health determinants within the communities, research models can be designed whereby data may be collected from the community members through surveys. Data analysis results are often used in the decision-making processes; they may be applied to inform quality improvement, tend in the operational processes, and the best treatment and care processes to be adopted.
All these medical approaches rely on the application of data. With the development of modern technologies in the healthcare system, data collection is usually done to ensure that patient’s records are well updated for further treatment processes. Patient’s data are usually collected from the point of admission to the time of discharge. All the above activities are statistical approaches that are meant to collect data to inform decision-making processes.
Jahan, S., Al-Saigul, A. M., & Suliman, A. A. (2016). Attitudes to statistics in primary health care physicians, Qassim province. Primary health care research & development, 17(4), 405-414.
Milic, N. M., Masic, S., Milin-Lazovic, J., Trajkovic, G., Bukumiric, Z., Savic, M., … & Ilic, A. (2016). The importance of medical students’ attitudes regarding cognitive competence for teaching applied statistics: multi-site study and meta-analysis. PLoS One, 11(10), e0164439.
Topic 4 DQ 1
Provide an example of experimental, quasi-experimental, and nonexperimental research from the GCU Library and explain how each research type differs from the others. When replying to peers, evaluate the effectiveness of the research design of the study for two of the examples provided.
REPLY TO DISCUSSION
Experimental Research: Experimental or Trial- Research is a form of research or study in which “the investigator directly controls selected conditions or characteristics of the environment, and observes the effects these changes have on other features of the problem at hand in order to determine causal relations” (Stoica, 2021). As the name implies Experimental research relies on running various tests and trials to come to a conclusion.
Example of an Experimental research study:
The Institute of Electrical and Electronics Engineers has conducted a study to research the effectiveness of cable-driven hip joint power-assisted exoskeletons. The experiments conducted to research this required three different conditions. The first condition was no exoskeleton, the second was exoskeleton opening and closing, The third condition was the effects of different experimental conditions on human joint angle, carbon dioxide exhalation, and sEMG. The results of these experiments showed the maximum angle difference of hip and knee was almost halved with the exoskeleton (3.6° with VS 6.1° without). Results also showed a 3.5% decrease in the overall carbon dioxide content in exhaled gas. Lastly, results showed The RMS values of the inferior gauze tail muscle and the quadriceps femoris muscle decreased by 51.40% and 42.55%, respectively (Ma et. al, 2022). With this information a conclusion can be reached that cable-driven hip joint power-assisted exoskeletons showed that motion deviation was small, muscle consumption was greatly reduced, and exoskeletons play a good auxiliary role in human walking.
Quasi-Experimental Research: Quasi-Experimental research is a form of study in which the aim is to evaluate interventions but that does not use randomization. “Quasi-experimental studies encompass a broad range of nonrandomized intervention studies. These designs are frequently used when it is not logistically feasible or ethical to conduct a randomized controlled trial” (Harris et. al, 2006).
Example of a Quasi-Experimental research study:
The National University of Singapore conducted a quasi-experimental research study to find the impact of the Scholarly Project® on medical students’ perception of research skills in Vietnam. To test this “A questionnaire evaluating the perception of fourteen research skills was given to participants in the first week, at midterm, and after finishing the Scholarly Project; students assessed their level on each skill using a 5-point Likert scale from 1 (lowest score) to 5 (highest score)” (Nguyen et. al, 2022). The results showed significantly high scores for 11 skills after participation in the Scholarly Project®.
Non-Experimental Research: Non-experimental research is research without the manipulation of independent variables, random assignment of participants to conditions, and/or orders of conditions.
Example of Non-Experimental research study:
A non-experimental research study was conducted by the BMJ journal to measure the effect of including OAT in The Joint Commission’s NPSGs on historically low rates of OAT initiation for individuals with incident atrial fibrillation (AF). This test was conducted using North Carolina State Health Plan claims data from 944 500 individuals enrolled between 1 January 2006 and 31 December 2010, supplemented with data from the Area Resource File and Online Survey, Certification and Reporting data network (Beadles et. al, 2014). The results showed OAT initiation was decreased (26.8%) for eligible individuals with incident atrial fibrilation in 2006–2008 but increased after NPSGs implementation (31.7%, p=0.022). OAT initiation was high but was lowered in the positive control group (67.5% vs 62.0%, p=0.003). Multivariate analysis resulted in a relative 11% (95% CI (4% to 18%), p<0.01) increase in OAT initiation for incident AF patients.
Beadles CA, Hassmiller Lich K, Viera AJ, et alA non-experimental study of oral anticoagulation therapy initiation before and after national patient safety goalsBMJ Open 2014;4:e003960. doi: 10.1136/bmjopen-2013-003960
Harris, A. D., McGregor, J. C., Perencevich, E. N., Furuno, J. P., Zhu, J., Peterson, D. E., & Finkelstein, J. (2006). The use and interpretation of quasi-experimental studies in medical informatics. Journal of the American Medical Informatics Association : JAMIA, 13(1), 16–23. https://doi.org/10.1197/jamia.M1749
Nguyen Tran Minh Duc, Khuu Hoang Viet, & Vuong Thi Ngoc Lan. (2022). Impact of Scholarly Project on students’ perception of research skills: A quasi-experimental study. The Asia Pacific Scholar, 7(4), 50–58. https://doi-org.lopes.idm.oclc.org/10.29060/TAPS.2022-7-4/OA2748
- Ma, A. Zhu, Y. Tu, J. Song, D. Dang and Y. Zhang, “System Design and Experimental Research of Cable-driven Hip Joint Power-assisted Exoskeleton,” 2022 19th International Conference on Ubiquitous Robots (UR), 2022, pp. 237-242, doi: 10.1109/UR55393.2022.9826254.
Stoica, I. (2021). Experimental (Trial) Research. Salem Press Encyclopedia. https://eds-p-ebscohost-com.lopes.idm.oclc.org/eds/detail/detail?vid=1&sid=d01c6dab-6686-47c9-b2fe-2f9a5c973400%40redis&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=89164212&db=ers