Uses of Statistical Information

Statistical information is gathered in a multiple ways - Uses of Statistical Information introduction. Information is received and compiled into reports to be translated into a simpler more understandable form. The purpose of this paper is to Identify the types of statistical data collected in a workplace setting, including the information is collected, and to discuss the advantages of accurate interpretation of data. When explaining statistics in a work place a little background must first explain a little about the company. First Coast Service Option is a contracted company for Medicare. Medicare (CMS) is a federal program in the United States of America that is a health insurance program for people over the age of 65, people over the age of 65 with a disability, or people of all ages with end-stage renal disease. Medicare has many sections but the parts directly connected to Medical review is Medicare Part A and Medicare Part B. Medicare part A works directly with the large corporations like the hospital as a company for a prescriber this is hospital insurance and Part B is more the physician as an individual this is medical insurance. Medicare provides medical coverage for over a 100 million people. First Coast Service option services a Medicare claims for Florida, Puerto Rico, and Virgin Islands. A Utilization review (UR) nurse decides what level of care is necessary and appropriate for patients. We make decisions to either approve or reject a diagnostic test, medication, or a specific treatment ( Statistics are used in most work settings for many different reasons. In the medical review setting statistics are used for various reasons like tracking a specific claim or a specific type of claim, Shareholders satisfaction, or employees’ satisfaction to name a few. The staff that provides results to the entire corporation performs these studies and evaluations periodically, depending on the situation or as often as daily. These evaluations are done to improve timeliness, accuracy and, cost effectiveness.

To monitor these studies both descriptive Statistics and inferential statistics are used ( Descriptive data is used to explain large amounts of data in a simple way and to recognize pattern in that data. In Medical Insurance descriptive data can be described as a 1000 claims coming into the mail department and use of descriptive data would give valuable information about the type of claim that came in, the provider and the amount of claims submitted by a specific provider in a specific span of time. This information is valuable in tracking possible Medicare fraud and in education for providers that have problems getting their claims paid. Descriptive statistics are used in specific large population of data. With the growing amount of claims that a processed daily; descriptive data would also help in tracking a specific claim for errors (Bennett, Briggs, & Triola 2009). Inferential statistics can be described as random samplings used a representative for a specific population. This type of statistic can be used many of ways in medical insurance. Each month six completed claims out of the 400-plus required are selected from each nurse to ensure accuracy and punctuality.

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This is later factored into the nurse’s annual review and bonus if a specific goal is maintained. The second example is when the system picks a specific provider to be audited based on an increased amount of claims from previous years, more than other professional in the same like category or just a random audit. During this audit a provider is sent a letter requesting payment made to him or her during a specific time period and then records are requested for further review. This type of random selection is understandable in the examples given above. Because of the massive amounts of data that comes in it is difficult and sometimes impossible to observe every nurse or physician. Instead the system or our review team attempts to get a representative sample, and it is used to represent their claim( Four levels of measurement are used in some way in Medicare. All four may not be used but with the amount of information reviewed all four levels in some way shape or form would have to be used. The four levels of measurement are interval, ratio, ordinal and, nominal. These are another way to separate data. The nominal measurement deals with categories. On part B of Medicare the goal of claims 80 a day so in order for the nurses to reach that obscene amount of claims per day they separate the work by provider, procedure code, and category. This helps the reviewer move through claims faster because she is not looking up multiple policies. Ordinal measure is data that can be assigned to numerical rank or categories.

This type of measurement can be described is used in the first in first out rule. Claims come in and are scanned with a specific dates called Julian dates. Medicare policies that as claims come in no matter the type/category they are work according to the date they were scanned into the system. So each day there is a Julian date attached and claims a separated by the Julian date; to ensure the first in first out policy is followed (Taylor, 2011). These last two measurements I could not correlate with the place of employment. Interval Level of Measurement is described as data that does not have an initial starting point, and it is assessed in increments. Ratio measurement is the last measurement it involves interval, ordinal measurements, and can be compared by describing it as three to four times a number; in changeable number like time. Advantages of accurate interpretation of statistical information are that it improves First Coast Service Option Company. Medicare Fraud would be at an all-time high due to no formal tracking. Without the basic information that statistics provides there could not be a standard of practice, no basic framework for how information is processed, and how to educate provides on Medicare policies. Conclusion

Statistics are needed in all aspects of health care. Statistic helps provide Medicare with the ability to improve decision-making in the medical review team, provide accurate, evidenced based information in a timely manner to the providers.

Bennett, J.O., Briggs, W.L., & Triola, M.F. (2009). Statistical Reasoning for Everyday Life (3rd ed.). Boston, MA: Pearson Education, Inc. Centers for Medicare and Medicaid Services Retrieved from: Taylor, C. (2011). Levels of Measurement. Retrieved from: statistics What are Inferential Statistics retrieved from:

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