Importance of Statistical Measures for Healthcare Administrator Essay

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In every firm, regardless of the industry, the gathered raw data needs organization, summarization, and visualization to turn the data into useful information. Evans and William (2014) show that statistics entails the knowledge associated with data gathering, organization, examination, clarification, and presentation. From a biblical perspective, statistics entails summing up and numbering. In Numbers 1:2, the Lord commanded Moses to, “Take ye the sum of all congregation of the children of Israel, after their families, by the house of their fathers, with the number of their names, every male by their polls” (King James Bible, 2017). The authors further point out that about Six Sigma, statistical understanding is significant since it helps comprehend possible data trends, patterns, and changes in a specific time (Evans and Williams, 2014). As a healthcare administrator, this knowledge is essential since it will assist in understanding how my organization compares its performance relative to its rivals, business units, and best-in-class benchmarks while acknowledging the association among various metrics.

Descriptive statistics represents how data is numerically and visually presented and might include charts, histograms, and frequency distributions in the organization and presentation of information. Database software like Access and Microsoft Excel facilitates how data can be summarized by every employee (Evans and Williams, 2014). As a healthcare administrator, the application of descriptive statics will be essential since it helps in minimizing the overall time required for inpatient admission, treatment, and discharge from the appropriate hospital department. Furthermore, under my administration, the application of such software will allow my subordinates to quickly access patient information on the precise admission and discharge times. That will significantly help my subordinates to control the severity of a patient’s health condition. Further, as Cox et al. (2014) show, by application of the theory of constraints, in tandem with statistical measures understanding, healthcare facilities can also provide patients with the needed care and grow their revenues. As such, under my administration, the employment of statistical measures will be critical. That will be the case since it will enable my facility to afford its patients with on-time appointments, allow for efficiency in the rooming process, and facilitate smooth handoffs.

The most significant descriptive statistics and formulas are mean, median, mode, and variance. Denoted by the Greek letter µ, mean is a summary representation of the whole dataset using a single number as the typical value (Kros and Rosenthal, 2016). Also known as the arithmetic average, the mean is also used to measure central tendency (Kros and Rosenthal, 2016). The Median represents the principal value or what is referred to as the 50th percentile (Evans and Williams, 2014). Under my administration, the median value will be the central value when half the data will be below it, and the remaining half value will be above it when the dataset will be arranged. Furthermore, in an odd number observation, my administration will show the median as the middle number of the sorted numbers. An even number observation becomes the mean of the two middle numbers (Evans and Williams, 2014). When using Excel, the median value can be found using the Excel function MEDIAN, which is the data range.

The most frequently occurring data is what constitutes the mode. As a healthcare administrator, the significance of the mode value will lie in the understanding that it consists of a relatively small number comprised of unique datasets. In a set of numbers where repeating numbers are few, the practical value of mode is also limited. When using Excel, the function MODE.SNGL or MODE.MULT represents the data range where the mode value can be obtained (Evans and Williams, 2014). Lastly, variance is a dispersion measure that relies on every data, and the larger the variance value, the more widely spread the data, and the more variability can be observed. However, this value cannot be provided using Excel and, as such, can only be calculated manually.

References

Cox, J. F., III, Robinson, T. M., & Maxwell, M. (2014). Family Practice Management, 21(5),18-22. Web.

Evans, J. R., & William, M. L. (2014). Introduction to Six Sigma & Process Improvement. 2nd ed. Cengage Learning.

King James Bible. (2017). King James Bible Online. Web.

Kros, J. F., & Rosenthal, D. A. (2016). Statistics for health care management and administration: Working with Excel (3rd ed.) Jossey-Bass.

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