Statistics in the Workplace Statistics are used in my workplace for many reasons. My primary function is to maximize the capabilities of the military treatment facility at the same time ensuring patients receive quality health care in a timely fashion whether it is direct or purchased care. All requests for purchased care are first reviewed for medical necessity and are then re-directed within the military treatment facility for right of first refusal. If the military treatment facility does not have the capability, the requests are forwarded to the managed care service contractor. We also monitor referral patterns, and identify trends relating to network leakage, network care recapture, continuity of care, and customer satisfaction. I work with a team of nurses who don’t work under a clinical directorate. We work under the business directorate. I help to decrease the government’s spending at my facility in particular, on healthcare costs and help to generate revenue in our facility. When purchased care costs are down, our revenue is up. If we see an opportunity to generate revenue in other ways we approach the directorate of business operations to see if we can implement a new plan.
Descriptive Statistics According to "Basic Statistical Concepts for Nurses" (2011), “Descriptive statistics are techniques which help the investigator to organize, summarize and describe measures of a sample. Here no predictions or inferences are made regarding population parameters. Descriptive statistics are used to summarize observations and to place these observations within context. The most common descriptive statistics include measures of central tendency and measures of variability” (Descriptive Statistics). An example of descriptive statistics at my workplace occurs when we collect data to identify trends in care being redirected to the purchased care network. We look at all specialty care networked out of our organization. The data is generally organized and entered into a table for viewing ease and clarity. We usually always have a handful of specialties that we simply cannot avoid purchasing care for so we look to identify trends to see why data increased in some months or declined in others. We usually determine that in those periods of influx that we have either lost a provider to deployment, provider shortages due to staffing issues in general, personal time off for providers during certain times of the year. These trends also help us to identify periods of time when fewer patients are seen not necessarily due to provider shortage but because the patients are not presenting for care. We have noticed that fewer patients come in for care during holiday seasons and that we may have an increase in orthopedic care due to increased injuries in the summer months. Of course most of this varies from year to year.
Inferential Statistics According to “Basic Statistical Concepts for Nurses” (2011), “Inferential statistics are mathematical procedures which help the investigator to predict or infer population parameters from sample measures. This is done by a process of inductive reasoning based on the mathematical theory of probability (Fowler, J., Jarvis, P. & Chevannes M. 2002)” (Inferential Statistics). An example of inferential statistics at my workplace recently was a climate survey completed by employees. The survey allows employees to voice their concerns anonymously. After the survey period is complete, the data is collected, reported, and the commanding officer holds a town hall meeting to discuss the findings and address concerns mentioned in the survey. I say this survey is inferential because we never get 100% participation from the employees. So the concerns of a few must represent the whole and the commanding officer must draw his own conclusions about the workplace climate based on the survey opinions of a few employees. I believe this last survey had only about 15% percent of our employees participate despite the many reminders sent out via e-mail. I am convinced that the CO can only generalize the workplace climate based on a 15% participation rate.
Levels of Measurement The levels of measurement are nominal level of measurement, ordinal level of measurement, interval level of measurement, and ratio level of measurement. A nominal level of measurement is, “A level of measurement for qualitative data that consist of names, labels, or categories only and cannot be ranked or ordered” (Bennett, Briggs, & Triola, 2009, Chapter 2). One example of a nominal level of measurement at my workplace is what we call family member prefix. It can be somewhat misleading because even active duty service members have a prefix. The prefix for a service member is 20, spouse is 30 and children are numbered in succession from first child (01) on to however many children the family may have. There are even numbers for extended family members like parents who may be dependent on the service member. An ordinal level of measure is, “A level of measurement for qualitative data that can be arranged in some order. It generally does not make sense to do computations with the data” (Bennett, Briggs, & Triola, 2009, Chapter 2). I believe an ordinal level of measure in reference to my above example would be that we always know that anyone with a family member prefix of 20 or an active duty service member has a different benefit than other beneficiaries. As long as it is MTF directed, it is not subjected to the standard scrutiny other requests are subjected to. Interval level of measurement is, “a level of measurement for quantitative data in which differences, or intervals, are meaningful but ratios are not. Data at this level have an arbitrary starting point” (Bennett, Briggs, & Triola, 2009, Chapter 2). An example of interval level of measurement in my workplace would be the measuring of temperatures in Fahrenheit or centigrade. Ratio level of measurement is, “a level of measurement for quantitative data in which both intervals and ratios are meaningful. Data at this level have a true zero point” (Bennett, Briggs, & Triola, 2009, Chapter 2). One example of a way that we could use ratio level data at my facility would be to determine the ages of people we treat within our facilities. For example, ratio of elderly to youth treated.
Conclusion
In my organization, statistics help us to understand what impacts our patients care. Whether we have service capability issues or patient satisfaction issues, statistics can help us to identify the problem and solve it. The collection of data for research and the use of statistics can support an assumption and change the way we operate on a day-to-day basis. Numbers in statistics when accurately interpreted equals facts.

References

Basic Statistical Concepts for Nurses. (2011). Retrieved from http://www.nursingplanet.com/Nursing_Research/basic_statistical_concepts_nurses2.html#Descriptive statistics
Bennett, J.O., Briggs, W.L., & Triola, M.F. (2009). Statistical Reasoning for Everyday Life (3rd ed.). Boston, MA: Pearson Education, Inc.