Week 2 Paper
Week 2 Paper: Uses of Statistical Information
Lynda Gruenewald-Schmitz
HCS/438
June 17, 2013
Amber Krasny
Uses of Statistical Information According to Bennett, Briggs & Triola (2009), descriptive statistics transforms data into a picture of information that is readily understandable using measures such as mean, median, mode, variation and standard deviation. Inferential statistics help researchers decide whether their outcomes are a result of factors planned within design of the study or determined by chance referencing probability values (P) to indicate significance of the change in results (Bennett, Briggs & Triola, 2009). “The two approaches are often used sequentially in that first, data are described with descriptive statistics, and then additional statistical manipulations are done to make inferences about the likelihood that the outcome was due to chance through inferential statistics” (Streiner & Norman, 1996). One example of descriptive statistics used in my workplace is studying our caregivers’ use of interpretative language services (ILS) for our non-English speaking patients. Trending indicated extraordinary expenses were incurred over time above our annual budgeted amount and a closer look at the data was required to better understand our next opportunities for more efficient management of our related costs. Utilization of agency translation services for Spanish speaking patients was identified as a major factor driving costs up and a study was completed looking at the number of ILS agency contacts by day of the week. Leaders wanted to know which days were the highest in demand for agency Spanish ILS before strategizing alternative staffing and resource plans. ILS agency contact data was collected from July 2012 through February 2013 by market per day of the week. The study reported a total of 4,491 agency contacts were made with non-english speaking Spanish patients. The data set included the following distribution
Lynda Gruenewald-Schmitz
HCS/438
June 17, 2013
Amber Krasny
Uses of Statistical Information According to Bennett, Briggs & Triola (2009), descriptive statistics transforms data into a picture of information that is readily understandable using measures such as mean, median, mode, variation and standard deviation. Inferential statistics help researchers decide whether their outcomes are a result of factors planned within design of the study or determined by chance referencing probability values (P) to indicate significance of the change in results (Bennett, Briggs & Triola, 2009). “The two approaches are often used sequentially in that first, data are described with descriptive statistics, and then additional statistical manipulations are done to make inferences about the likelihood that the outcome was due to chance through inferential statistics” (Streiner & Norman, 1996). One example of descriptive statistics used in my workplace is studying our caregivers’ use of interpretative language services (ILS) for our non-English speaking patients. Trending indicated extraordinary expenses were incurred over time above our annual budgeted amount and a closer look at the data was required to better understand our next opportunities for more efficient management of our related costs. Utilization of agency translation services for Spanish speaking patients was identified as a major factor driving costs up and a study was completed looking at the number of ILS agency contacts by day of the week. Leaders wanted to know which days were the highest in demand for agency Spanish ILS before strategizing alternative staffing and resource plans. ILS agency contact data was collected from July 2012 through February 2013 by market per day of the week. The study reported a total of 4,491 agency contacts were made with non-english speaking Spanish patients. The data set included the following distribution
References: American diabetes association. (2013). Retrieved from http://www.diabetes.org/living- with-diabetes/treatment-and-care/blood-glucose-control/a1c/ Bennett, J.O., Briggs, W.L., & Triola, M.F. (2009). Statistical reasoning for everyday life (3rd ed.). Boston, MA: Pearson Education. Streiner, D. & Norman, G. (1996). PDQ Epidemiology (2nd Edition). St. Louis: Mosby.