Statistics is everywhere in a person’s daily life. As Stated by Bennett, Briggs, and Triola (2009), “you’ll be hard-pressed to think of any topic that is not linked with statistics in some important way” (p. 2). The hospital, which I am, employed this holds to be true. I work in an ICU, so I deal with the sickest of the sick. Statistics are a big part of this departments care and education. Each day statistics is utilized through descriptive statistics, inferential statistics, and the level of statistical measurements.
The advantages are seen best when educating and evaluating the progress of care for the patient and families. There is not a day that does not go by which I do not use some statistical figure or method with education or care of my patient’s. In a critical care unit, I look at trends of labs in numerical format as well as graphs or chart format too. Looking at trends of labs lets care providers as the health care provider understand what direction the health of a patient is going in.
If there is a spike or drop in labs, vital signs, or health we look at graphs or charts to see if there is a correlation with meds given, abnormal labs, or change in vent settings. We look at mean and mode for renal patients to determine where a baseline creatinine clearance level may be. The doctor and nurses use law of averages when discussing possibilities and prognosis of patients. Percentages are used every day in the workplace. Example of percentages, renal doctor may estimate a renal patient possessing 20% kidney function.
Another example is an echo is done and the ejection fraction of a patient cardiac patient is 35%, which is below the wanted 50-55%. This gives us an idea of how efficient or inefficient the heart is pumping blood. These are the more popular statistical applications I utilize daily in the ICU I work in on. There are many more applications used and with each specialty (e. g. , Cancer studies/testing, transplants, respiratory compliance, and etc…) a different statistical method or figure may be used.
By definition according to Bennett, Briggs, and Triola (2009), “descriptive statistics deals with describing raw data in the form of graphics and sample statistics” (By the way…, p. 7). An example of descriptive statistics in my hospital would be the statistical figures for comparing length of stays for patients with communal acquired pneumonia versus hospital acquired pneumonia. As we collect the raw data for each, we can look and see that on average hospital acquired pneumonia patients stay seven days longer than communal acquired pneumonia patients.
Why is this important you might ask? Cost effectiveness for patient and hospital both. When many of population does not have insurance or poor insurance coverage the hospital has to absorb the remaining balance. Looking at this study and seeing the length of stay and why, the facility can make appropriate attempts to shorten the gap, thus losing less money. Inferential statistics as stated by Bennett, Briggs, and Triola (2009), “deals with inferring (or estimating) population parameters from sample data” (By the way…, p. 7).
An example of inferential statistics utilized in my ICU work environment is; looking at the raw data for creatinine clearance levels in renal disease patients within the hospital (sample population). Looking at these sample populations data over a year’s period, one could infer that this is what the rest of the renal patients are like in the community (city). By looking at the sample data of creatinine clearance, a doctor or health care provider can see how efficiently the dialysis is working for patients and make evidence-based changes that will benefit the entire population.
The data collected can also show the health care provider how compliant the population is, as an entirety, and educate the community accordingly. This data will show where the baseline is along with any outliers or abnormally large or small (range) numbers compared to the baseline number. When looking at these outliers, a doctor may correlate the outliers with a change in dialysis protocols or new medications given to patients, or compliance, or lack of compliance with population, which means the patient and family needs further education.
Any charts with the data may show a trend with the baseline number increasing over the time frame tested and conclude change is needed somewhere because the baseline numbers are worsening. Taking a year’s worth of data and looking at the range, mean, median, and mode to assessing the effectiveness of care compared to previous years will give doctors and other health care providers the needed knowledge to make effective changes for the future. The four levels of statistical measurement are: nominal, ordinal, interval, and ratio.
I will attempt to give an example of each of the four levels of measurement in my place of work. A “nominal” example would be patient names and the unit in which he or she resides in for his or her stay at the hospital. Example of this is Maggie Mae, critical care unit; Fred Jones Surgical/Ortho unit. An “ordinal” example seen in my place of work would be names of patients and the acuity level (care needed) of each patient. Example is, Maggie has a higher acuity level and Fred has a lower level acuity of care needed.
An “interval” level example in the workplace would be the temperatures of patients. At 9 a. m. the temp was 99. 8 degrees Fahrenheit and 12 p. m. it was 101. 1. This has an increased interval (difference) of 1. 3 degrees Fahrenheit in a 3-hour period. The last level of measurement example is ratio. A ratio example at the hospital would be Peep (Positive End Expiratory Pressure) being given to patients on a ventilator with the true zero being no support and adding one-point to get the required aeration response from the patient.
Advantages of accurate statistical information in a hospital setting such as the one I work in is seen best when evaluating educational material, lengths of stay, and labs of patients just to name a few. Placing a percentage on the comprehension of educational material is crucial. This makes sure patients and families fully understand what is needed to be done during and post hospital stay, to ensure the best quality of life. Example being, Maggie, and family only understands 1of 4 pages or 25% of the printed material, further education is needed.
Statistics describing Lengths of stay can be very influential with cost management along with noting where further cut backs or work needs to be done for saving money for patients and hospital alike. Looking at labs in a quantitative (interval and ratio) manner can very much help dictate care plans and the progress toward getting patients well. Statistics are very influential in all aspects and have many advantages in many situations in a hospital.