Use of Statistical Information
HCS/438
August 6, 2012

Use of Statistical Information
Statistics is defined as “the science of collecting, organizing, and interpreting data” (Bennett, Briggs, & Triola, 2009). For most patients and their families, the process of healthcare appears simple. People with illnesses are admitted into a hospital facility and a specific course of treatment is identified and the care is carried out by a team of physicians, nurses, and social workers. What is not noticed is a specialized resource team aimed at keeping all patients safe throughout the course of their hospitalization. This paper will identify how statistics are utilized in the infection prevention setting, identify one example of descriptive statistics, identify one example of inferential statistics, explain data at each of the four levels of measurement and describe the advantages of accurate interpretation of statistical information to improve decision making in the workplace.
How Are Statistics Used in Your Workplace
There are many uses for statistical application in the field of infection prevention and control. The purpose of infection prevention and control is to put into place policies and procedures that minimize the spread of infections, especially in the hospital setting. The primary function of infection prevention and control surveillance is to reduce the occurrence of infections by using risk factors and implementation of risk-risk reduction measures and the effectiveness of interventions (“Center for Disease Control”, 2012). Two examples of how statistics are utilized are the hospital-wide surveillance of central line associated blood stream infections (CLABSI) and catheter related urinary tract infections (CAUTI). Infection rates of CLABSI and CAUTI are reported to senior leadership on a monthly basis; if an increase in infection rates is noted then specific interventions are implemented to reduce the rates. Examples would be staff and patient education, an increase in monitoring of hand hygiene compliance and isolation practices.
What is One Example of Descriptive Statistics Used in Your Workplace
According to Bennett, Briggs & Triola (2009), descriptive statistics deal with describing raw data in the form of graphics and sample statistics. A specific example of descriptive statistics relating to the field of infection prevention and control would be displaying the rate of Methicillin-resistant Staphylococcus aureus (MRSA) hospital acquired infections (HAI’s) to the senior leadership on a monthly basis in graph form. What is One Example of Inferential Statistics Used in Your Workplace
Inferential statistics deals with inferring (or estimating) population parameters from sample data (Bennett, Briggs & Triola (2009). Senior leadership could make a recommendation based upon the results of the descriptive data to begin a staff education process related to proper hand hygiene and contact isolation practices to improve the MRSA HAI rate.
How is Data at Each of the Four Levels of Measurement Used in Your Workplace
The nominal level of measurement is pieces of information that consists of name, labels, or categories and involves no ranking or ordering of the data (Bennett, Briggs & Triola, 2009). Examples of this type of data would be the use of medical record numbers or social security numbers as a form of patient identification. The data is displayed in no particular order and is completely random.
Bennett, Briggs & Triola (2009) describe ordinal level of measurement as data with a ranking or ordering scheme, such as star ratings of movies or restaurants. The ordinal level of measurement provides a ranking system and does not allow determination of the precise levels of difference. The most common example of ordinal data is the use of a daily patient census. The census is displayed according to the room number location listing from lowest to highest.
Other information included in the census listing is the patient’s name, medical record number and admission date.
With the interval level of measurement, the differences between the numbers do matter, but the ratios and zero point do not (Bennett, Briggs & Triola, 2009). Actual temperatures using the Census and Fahrenheit scales are common. Data at this level has an arbitrary zero point. The Sterile Processing Service (SPS) uses the interval level of measurement when mixing the cleaning and disinfectant solutions for the reprocessing of reusable medical equipment. The facilities Standard Operating Procedures (SOP’s) require this process to assure the complete decontamination of equipment.
The ratio level of measurement applies to quantitative data in which both intervals and ratios are meaningful; data at the level have a true zero point (Bennett, Briggs & Triola, 2009). Daily weights are taken on patients undergoing antibiotic therapy. These weights are used by pharmacy to adjust dosages on certain antibiotics that require weight-bases therapies. The facility also uses this data to determine which patients are losing weight and might be in the need of a consult from the nutritionist.
What are the Advantages of Accurate Interpretation of Statistical Information to Improve Decision Making in Your Organization
Accurate interpretation of data is essential to provide the highest quality of patient care as it relates to infection prevention and control. The primary responsibility of this department is to use the data obtained and bring forth proper policies and procedures that reduce the risk of patients acquiring an infection during their hospitalization. “At its most basic level, statistical quality control is rooted in the graphical and statistical analysis of process data for the purposes of understanding, monitoring, and improving process performance-general objectives that in essence are quite similar to those of epidemiology” (Benneyan,1998) .

References
Bennett, J. O., Briggs, W. L., & Triola, M. F. (2009). Statistical Reasoning for Everyday Life (3rd ed.). Boston, MA: Pearson Education, Inc..
Benneyan, J. C. (1998, March). Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part I: Introduction and Basic Theory. Infection Control and Hospital Epidemiology, 19(3), 194-214.
Centers for Disease Control. (2012). Retrieved from http://www.c