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Uses of Statistical Information

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Uses of Statistical Information
HCS/438
October25, 2010
Ray Harvey

Uses of Statistical Information
Statistics are an important part of any company or organization. Statistics can be used to measure just about anything within a company. Any chosen topic within an organization could have a statistical study done. Specific people within an organization are those that do the studies and post results of the statistics to inform other people within the organization. I work for a hospice organization that is non-profit and I am sure that they are constantly doing studies and tracking statistics of the patients and families that we provide care for daily. This paper will show some possible ways that this hospice organization can use descriptive and inferential statistics.
Descriptive Statistics
Descriptive statistics is using graphs to describe raw data from research and studies (Bennett, Briggs,& Triola, 2009). One area that we use descriptive statistics and that I can look at deals with patient and family satisfaction. Hospice surveys the patients and the patient’s families that we have taken care of and obtains feedback about the experience they had with hospice. Oftentimes this information comes from the family members because the patients usually pass away before we get this information. This is done with each home care and each inpatient unit that we have. The company takes the feedback for three months and comes up with the mean for those three months to let us know how we are doing in this area. One of the reasons this is done every three months is that sometimes the census is low and there may not be as much data for one month. We can see how we are doing as an individual unit to let us know how we may need to improve. The graph with this information is posted on our internal intranet for all employees to see. These are posted four times a year, so we can see how we have changed in patient and family satisfaction for the whole year. At the end of the year, we are able to get a mean for the whole year to see how we did for the year. This can then be compared year to year as well.
Inferential Statistics
Inferential statistics is taking sample data and using it to infer parameters on a larger population (Bennett, Briggs, & Triola, 2099). This uses specific procedures such as linear regression, t-test, analysis of variance, logistic regression, survival analysis, and chi-square test. Inferential statistics could be used in hospice by using a t-test to compare the means of our elderly population that have private insurance, need government assistance or have money saved to pay for medical expenses. A separate survey can be done with a group of our female elderly patients and our male elderly patients to get information on how they pay for medical expenses. Another survey can be done separately with male and female elderly persons not a part of hospice care. This information can then be compared based on the different forms of payments between the male and female patients as well as the hospice and non-hospice patients. Estimates can be made about male and female elderly persons in the bigger population based on these comparisons of these small groups that were surveyed.
Four Levels of Measurement
In hospice care, nominal measurement could be used by classifying our numbers of patients who are satisfied or unsatisfied with their hospice care by simply using an S or U instead of the numbers. Given the same scenario of patients and families being satisfied or not satisfied with the care they received, we could use ordinal measurement to rank their satisfaction levels one being unsatisfied and five being completely satisfied. This would give an order to the measurement. If we were to use interval measurement with this scenario, we would show on the graph or chart that the intervals between a patients stay of two to four weeks has the same interval of the patient who stayed with hospice six to eight weeks. These two time frames have the same interval of two weeks between them. Using this same scenario, we can also use ratio measurement because there could be an absolute value of zero. There can be a time when we had no patients that stayed for two to four weeks within a two month period. We could show the ratio, given the amount of time patients were with hospice.
Advantages of Accurate Interpretation
Advantages of accurate interpretation of statistical information are important with any organization when making decisions. In hospice care, we need to have accurate statistics to make informed decisions that will ultimately affect our patients. If decisions are made that do not come from accurate statistics such as patients who may need financial help, we could hurt the population of people around our area who need hospice services. The advantage to accurate interpretation of statistics also helps our organization be able to see areas that need improvement and areas that we are excelling in. No organization wants to make changes to something that is working well because the statistics drawn upon were inaccurate.
Conclusion
Statistics in any organization are very important for the success of the organization. If statistics are not used within an organization, there is nothing to show where improvements can be made as well as being able to see what is working well. I know the hospice agency that I work with are constantly using statistics to see where we stand with other hospice agencies, how we are doing within our own agency and also how we are helping the people around us who need hospice care. I don’t see many of the statistics that are done within our hospice agency, but I know that they are there because results are shared in some of our quarterly meetings.

References
Bennett, J.O., Briggs, W.L., & Triola, M.F. (2009). Statistical Reasoning for Everyday Life (3rd ed.). Boston MA: Pearson Education, Inc.

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