Uses of Statistics in the Workplace
Uses of Statistics in the Workplace
Statistics is defined by Bennett, Briggs, & Triola (2009), as “the science of collecting, organizing, and interpreting data” (p. 1). Almost every profession uses statistics in some way to guide in making good decisions based on current research. The nursing profession relies heavily on current research to guide patient care with the integration of evidenced-based practice. Statistics provide valuable information to caregivers to help them understand, plan, evaluate, and improve the quality of patient care.
In the acute care hospital setting there are ongoing measurements of such things as patient satisfaction, hand washing compliance rates, catheter acquired urinary tract infections, and central access infection rates, just to name a few. The collection of this data involves descriptive statistics, inferential statistics, and levels of measurements. Acute care hospitals use descriptive statistics in many ways. Descriptive statistics describes raw data in the form of samples or graphs (Bennett, Briggs, & Triola, 2009).
One area in which they are used in the hospital is to evaluate hand washing compliance of health care providers. According to Vincent (2003), nosocomial infections occur in approximately 30% of patients in the intensive care setting and are associated with increased morbidity and mortality. Research shows that effective hand washing can prevent many hospital acquired infections (Vitez, 2010). In the hospital setting, hand washing compliance is monitored on an ongoing basis.
Health care workers who come in contact with patients are observed by an unidentified member of the staff who monitors the subject upon entering and leaving a patient room. Hand hygiene can be performed by either by washing with soap and water or use of hand sanitizers. The expectation is that the subject will wash their hands upon entering the room and upon leaving the room. The subject must be monitored both entering and leaving the room for the observation to be included in the data. Initial hand washing data showed poor staff compliance.
Employees were lacking in hand hygiene and putting patients at risk (Vitez, 2010). Based on the results of early observations, a plan was implemented to increase staff compliance. Education was provided to increase awareness of the importance of hand washing and frequent reminders are given in the form of screen saver messages and signs posted at the entrance of every room. Interventions have also been implemented such as conveniently placing hand sanitizer containers outside of every room and throughout the hallways of the institution.
Recent monthly hand hygiene compliance rates are generated and have improved to 85% -90% hospital wide. Use of these descriptive statistics using raw data on hand hygiene rates has been an important tool in increasing awareness of the importance of hand hygiene to the overall safety of our patients. Hospitals are safety and quality driven. Several research studies have shown a direct relation to the skill and education of the nursing staff and a decrease in mortality (McHugh & Lake, 2010). Inferential statistics involves making predictions based on information obtained in a smaller sample (Bennett, Briggs, & Triola, 2009).
This information and the inference of better patient outcomes have prompted many hospitals to require nursing staff to attain a bachelor’s of science in nursing. The research suggests a positive correlation between critical thinking skills and nurses with a bachelor’s of science degree and positive patient outcomes (McHugh & Lake, 2010). The institution where I am employed, and many institutions in our tri-state area, is using the findings of these inferential statistics to require that all nurses in their employ obtain a bachelor’s of science in nursing in an effort to provide patients with the best possible outcome.
Those in the health care profession, and those involved in nursing research, have many uses for the four levels of measurement in statistics. The four levels of measurement in statistics include nominal, ordinal, interval, and ratio (Bennett, Briggs, & Triola, 2009). The nominal level of measurement is the simplest level of measurement that involves variables, or labels, to classify data in a qualitative way (Bennett, Briggs, & Triola, 2009). Nominal variables include such things as categories of people, race, gender, or age.
In the hospital setting, the nominal level of measurement is used most obviously when completing a patient history which asks the patients name, sex, marital status, and blood type. The ordinal level of measurement assesses data incrementally and puts data in order either from low to high or high to low in a ranking system (Bennett, Briggs, & Triola, 2009). This level of measurement is used in the hospital setting to measure pain perception and in patient satisfaction surveys.
There has been increasing emphasis on the use of patient satisfactions surveys to assess the quality of health care and many facilities have implemented improvement projects in relation to such things as reception skills, food services, housekeeping, and reorganization of hospital discharge procedures (Gray, Richmond, & Ebbage, 2010). These scores reflect the patient’s subjective perception of their hospital experience and his or her likeliness to recommend the facility to family members and friends.
Ordinal levels of measurement are also used to rank hospital performance in several areas including hospital acquired infections and readmission rates (U. S. Department of Health and Human Services, n. d. ). These rankings are reported to the public and may influence a health care consumer in their decision of where to seek their medical care. Interval levels of measurement apply quantitative data in meaningful intervals without reference to ratios and no set point for zero; variables within this level of measurement are assessed at equal intervals (Bennett, Briggs, & Triola, 2009).
The obvious example in the health care field of an interval level of measurement would be that of a thermometer or a calendar. Using the hand hygiene information mentioned earlier, the information is presented to the staff using a grading system that is broken down into intervals. Each interval is identified by a color. The scale begins at 60%. Units with a compliance ranking of 60-79% are given the color red. Units with a compliance ranking of 80-89% are given the color yellow. Green is given to any unit that has a hand washing compliance ranking of 90% or greater.
This interval level of measurement ranks each unit and allows them to compare their rankings with other units in the facility. As incentive for improvement, departments with consistent compliance rankings of 90% or above have been given rewards such as gift cards and luncheons. Ratio levels of measurement are similar to interval levels but a zero point does exist (Bennett, Briggs, & Triola, 2009). Ratio levels of measurement apply to quantitative data characterized by intervals that are assessed incrementally with equal distances between the increments (Bennett, Briggs, & Triola, 2009).
In the hospital setting, nurses routinely use ratio levels of measurement such as the patients weight, height, temperature, blood pressure, and respiratory rate. In conclusion, numerous statistics are collected and analyzed in the health care setting. Accurate statistics provide information regarding patient satisfaction, patient safety, and patient outcomes. Using this information to identify areas for improvement, planning, and implementing changes in care and practice will improve the quality of care, decrease morbidity, and improve patient outcomes.
Subject: Health care,
University/College: University of Arkansas System
Type of paper: Thesis/Dissertation Chapter
Date: 1 December 2016
We will write a custom essay sample on Uses of Statistics in the Workplace
for only $16.38 $12.9/page