A statistic involves viewing and comprehending data. Data links a situation or an event to a similar one that was encountered in the past. Data is important in establishing the most effective methods when administering medication. Decision making in health care uses both quantitative and qualitative statistics. while quantitative research is involved with numerical data collected to describe specific population samples, qualitative statistics are used to summarize the costs of health goods and services, efficacy and usefulness of the medical goods and services.
Statistical analysis is used by, medical organizations tom gauge their performances and outcomes overtime. In implementing data driven and quality continuous improvements, hospitals and other health care providers are able to maximize their efficiency. To this end, scientific methods are employed by researchers to gather data on human population samples.
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The health sector reaps benefits from understanding consumer market characteristics such as age, sex, race, income and disabilities. These varied "demographic" statistics can predict the types of services that people are using and the level of care that is affordable to them (Pett, 2015). For effective production of goods and services, statistics are vital as a means drawing a line between success and failure in a health organization.
Statistics can also be used as a future projection on profitability by quality improvement managers. This can be done through benchmarks or setting standards of service excellence. It could also be derived through determination of the mix of goods and services to produce, resources to be used in the producing them and the target population. Statistical analysis is also prudent in innovations in the medical field (Groves, et al. 2016). The collected data that is reported in clinical trials of new technologies and treatments can be used to weigh risks versus benefits this steers developers to act upon a highly competitive product lines. Indirectly, pricing is to a large extent influenced by statistics since it describes consumer demand quantitatively.
There are varied ways in which we apply statistical knowledge in nursing. Sometimes a nurse makes some observations to a client that do not require serious concern. Using statistics, the nurse is able to judge whether or not immediate medical attention is required. For example, a nurse will be able to know if by putting a patient in an emergency room for a certain duration of time will worsen their condition or not in order to prioritize treatment. Statistics is also applied in clinical nursing to determine whether a commonly used method should be changed or if protocols should be revised ( Batarseh, & Latif, 2016) . Statistics are also used to analyze the trends in the health patterns of a patient. Research in nursing processes and procedures too need statistics. For example, if a nurse is doing wound care, there has been research using statistics on the outcomes of patients for different kinds of procedures for wound care.
In collecting data, there are various ways that are used. The most common are the hospital length of stay and discharge destinations. These 2 are important since they measure outcomes in order to evaluate the effectiveness and efficiency of health services. Hospital administrative data is also used as a source of data collection but not largely.
It is imperative to note that as a medic, statistical knowledge is important in the day to day operations. Statistics are used to deciding the type of medication to administer especially to patients with chronic diseases who might need stronger type of medication with each subsequent ailing.igt is also important to managers in order to improve the efficiency of health care systems.
References
Batarseh, F. A., & Latif, E. A. (2016). Assessing the quality of service using big data analytics: with application to healthcare. Big Data Research , 4 , 13-24.
Groves, P., Kayyali, B., Knott, D., & Kuiken, S. V. (2016). The'big data'revolution in healthcare: Accelerating value and innovation.
Pett, M. A. (2015). Nonparametric statistics for health care research: Statistics for small samples and unusual distributions . Sage Publications.
