Use of statistical Applications

Custom Student Mr. Teacher ENG 1001-04 12 March 2016

Use of statistical Applications

Uses of Statistical Information Paper
According to Bennett, Briggs, & Triola, (2009) “Statistics is the science of collecting, organizing and interpreting data.” (p. 2). The purpose of statistics in healthcare is to help integrate management and health care activities. This paper will discuss the use of statistics in health care especially in skilled and Long-term care where the nurse works, where by data is collected on daily basis to monitor trends, improve care, and customer satisfaction. Descriptive statistics at work place helps to analyze, describe or summarize numerical data in a meaningful way. Description of four levels of data measurement will be discussed, to include nominal, ordinal, interval, and ratio, as well as the advantages of accurate interpretation of statistical information in improvement of decision-making at work place. Use of Statistics at work place

Collection, analyzing, and interpretation of data are important functions at the skilled facility. The nurses are major contributors of data that is either, generated electronically or by the health record department staff. Health care provision today is centered on evidence-based care, which continues to influence the type of care patients receive. Statistical information enables the health care providers to plan, formulate current and future policies, improve patient care through evidence-based studies, evaluate, and improve on customer care, and for compliance purposes. Examples of data collected on daily basis are the daily patient census that summarizes the total number of patients per unit by the end of the day computed manually at 12 midnight.

This reporting tool is presented in tabular format and shows all new admissions, readmissions, in-house transfers, transfers out to other levels of care, discharges, deaths, or missing patients. Another example of statistical information at the work place is patients’ progress reports that consist of daily assessments, risk factors, skin problems, weight loss, and changes in level of care, and drug adverse reactions or errors. This report is gathered, and summarized by the Minimum Data Set (MDS) staff. This report is a stringent regulatory requirement by the Medicare, and Medicaid in order to facilitate the payment process for refunds (Center for Medicare & Medicaid Services, 2008). This type of report is also used for planning, compliance purposes, as well evaluation of care for better outcomes. Descriptive Statistics

Descriptive statistics is a graphical summary that shows the spread of data and numerical summary that measures data value or describes the spread of data (Bennett et al, 2009). One example is the central tendency also called Location Statistics. It consists of the mean, median and mode (McHugh, 2002). The mode is useful in nominal scale. The median is the exact mid-point while the mean is used in all sorts of computation and data analysis. An example used at place of work is to find a diagnosis of elderly patients who presents with increased confusion, lethargy, fever, urinary frequency, irritability, restlessness, and poor intake of food and water. The most common distribution will lead to Urinary Tract Infection diagnosis. These constellations of symptoms represent the central tendency. Levels of Measurement

Another way to classify data is by their level of measurement (Bennett, et al, 2009). The first level of measurement is the nominal measurement. According to McHugh, (2003), the word nominal means “name” (p. 35). Nominal date consist of names, labels, or categories only. Qualitative data is gathered rather than quantitative. In order to measure data in nominal level, the variables should be able to divide into distinct groups. The measure provides one statistical value for each occurring variable (McHugh, 2003). Example of nominal level of measurement at workplace is measurement of quality of pain. Patients can describe pain as dull, sharp, throbbing, stinging, constant, burning, or radiating. The level of pain is not nominal, but the characteristic of pain is expressed within a group.

The second level of measurement is ordinal which Bennett, et al, (2009) defines as “qualitative data that can be arranged in some order (such as high or low)” p. 4. In this level, categories are represented numerically, and can serve as labels (McHugh, 2003). Still using the pain example patients may be asked to rate pain on a scale of 0 to 10. Ten signifies much more pain than two would. Graphically the categories will be represented as no pain, mild pain, moderate pain, severe pain or extreme pain, with the variable varying depending on the total number of patients assessed for pain at each category (McHugh, 2003).

The next level of measurement is the interval measurement, which applies to quantitative data in which intervals are meaningful but ratios are not (Bennett et al, 2009). In comparison interval measure has both categories and magnitude like the nominal and ordinal measure, but interval measure data have arbitrary zero point. Examples would be patients’ temperature, blood glucose levels, Oxygen level just to mention a few. Lastly, the ratio level of measurement refers to qualitative data in which both intervals and ratios are meaningful. Data at this level have a true zero point (Bennett et al, 2009). This kind of measurement is commonly used in healthcare to measure lengths of incision sites, cuts wound, implants, and heights of patients. Advantages of Accurate Statistical Information

Data presented accurately should be interpreted accurately. Accurate interpretation of statistical information ensures the appropriate interventions are employed in making organizational decisions and policies that will bring about the desired changes or solutions to identified problems. Accurate interpretation of statistical information is cost effective in terms of time and finances. Accurately interpreted statistical information enables managers to make appropriate decisions in a timely manner. This saves companies the expense of a repeat research or implementation of irrelevant plans of action for identified organizational needs. Accurate statistical interpretation helps in forecasting and budgeting to avoid future losses. Conclusion

Statistics continue to play a major role in health care. Collection, organizing, and interpreting data helps in identifying problem areas, formulation of policies, and decision making to achieve desired outcomes within an organization. Descriptive statistics assist with the organization and summarizing of data enabling the researcher or consumer understand more about data. Different statistics require different levels of measurement. The choice of level of measurement depends on the variables. With increased need for evidence based-care, statistical information continues to influence type of care patients receive as well as organizational planning, and budgeting.


Bennett, J. O., Briggs, W. L., & Triola, M. F. (2009). Statistical reasoning for everyday life (3rded.). Boston, MA: Pearson Education. McHugh, M. L. (2003). Descriptive statistics, part I. Journal for Specialists in Pediatric Nursing, 8(1), 35. Retrieved from ProQuest Central. McHugh, M. L. (2003). Descriptive Statistics, part II: Most commonly used descriptive Statistics. Journal for Specialists in Pediatric Nursing, 8(3), 111.

Bennett, J. O., Briggs, W. L., & Triola, M. F. (2009). Statistical reasoning for everyday life (3rd ed.). Boston, MA: Pearson Education. Center for Disease Control. (2010,April). Nation Center for chronic Disease Prevention and Health Promotion. Retrieved from Easton, V. J., & McCoil, J. I. (2010). Steps. Statistical Glossary, 12(2), 2-3. Retrieved from McHugh, M. L. (2003). Descriptive Statistics, part II: Most Commonly Used Descriptive Statistics. Journal for Specialists in Pediatric Nursing, 8(3), 111. McHugh, M. L. (2003). Descriptive statistics, part I. Journal for Specialists in Pediatric Nursing, 8(1), 35.


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  • University/College: University of Chicago

  • Type of paper: Thesis/Dissertation Chapter

  • Date: 12 March 2016

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