Using Statistical Information

Statistics have become critical to the operation of any medical facility - Using Statistical Information introduction. At Samaritan Medical Center (SMC), we routinely look at data to guide our workflow, staffing and patient satisfaction. One of the most commonly referenced statistical databases used at our facility is Press Ganey. Press Ganey utilizes a mailed survey to a random selection of discharged patients to gauge the quality of care given to a given patient, based on their perception of the hospital visit. (Press Ganey, 2010).

The Press Ganey data is generated from a comprehensive patient survey tool, which is divided into multiple areas. This division looks at the patient’s visit by location, provider and individual elements in each area. (Press Ganey, 2010). The resulting data is then presented in different frequency tables. This data encompasses both descriptive and inferential statistics. A descriptive statistic is used to describe a specific set of measurements. (Bennett, 2009). An example of how Press Ganey uses descriptive statistics would be our facility’s quarterly ranking percentile.

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This information is presented based upon the overall patient satisfaction rating averaged from the responses received from the patient surveys that are returned in the mail. Thus this data is born out of the inferential statistics gathered in the survey. This result is given in a percentage of satisfaction, which is presented every quarter. For example, SMC’s quarterly patient satisfaction rating was 86%. This number is then weighed against other facilities that have the same approximate volume and similar demographics. Based on that comparison, we receive a ranking as compared to these other facilities.

Our overall ranking of 86% put SMC in the 60th percentile. Press Ganey also presents data in the form of inferential statistics. Inferential statistics use data gathered from a sample to make inferences about a larger population from which the sample was drawn. (Bennett, 2009). In the case of Press Ganey, a survey is mailed out to a random sampling of patients. Based upon the returned results, Press Ganey draws conclusions based on this data and uses the resulting information to help an organization to focus its improvement efforts.

This will then lead o a facility’s better understanding of their patients’ perceptions of their care experience. (Press Ganey, 2010). By looking at the random cross section, Press Ganey provides SMC with an overall picture of the quality of the care we provide our patients. At SMC, there are instances where we utilize all four levels of measurement. All four levels have value and are used on an almost daily basis. The most basic is nominal measurement, which is a measurement that results in basic identifiers. For example, we use a nominal measurement when a patient registers for service in the emergency department.

In the registration process, as number is assigned for a specific value, like race, gender or marital status. A “1” is assigned for male patients and a “2” is assigned for female patients. An example of an ordinal measurement would be the pain scale utilized by the facility. This is an assessment of the patient’s pain level, in a scale of 0 to 10, with 0 being pain free and 10 being the worst pain of the individual’s life. These types of scales do not represent a measurable quantity, but an individual’s perception of pain. (Bennett, 2009).

What one patient rates a 7, another could rate a 9 on the pain scale. An example of an interval scale of measurement would be the patient’s temperature. An interval scale is a measurement where zero only represents an additional point of measurement and not the lowest possible value. In other words, negative numbers are possible. (Bennett, 2010). Even though a Celsius temperature of zero (or 32o Fahrenheit) would be obviously fatal, the scale is sound as an interval measurement. Finally, an example of a ratio scale of measurement would be would measurements.

A ratio scale also has quantity and an equality of units, like an interval, but it does have an absolute zero and cannot progress into negative numbers. (Bennett, 2009). A wound is measured in length, width and depth, which provides ration data for the specific wound. All of the above examples gather data on a specific patient. However, as this information is entered into the electronic medical record, statistical data can be drawn form that database and applied to areas of interest, used to gather information on a the populace served or used to improve workflow and procedures.

All of the above data collection and statistical measurements hinge on accuracy. Obviously the more accurate the gathered data is, the more accurate your statistical analysis and subsequent interpretation will be. With accurate data, your health care agency can make more informed decisions. This is a given. Where we run into difficulty is with the accuracy of the data collected. For example, Press Ganey relies of surveys returned from a supposedly random selection of patients.

However, for the emergency department, we can see a patient’s length of stay in the ED in association with their survey. It is interesting that the number of negative surveys returned is directly related to the patient’s length of stay. In other words, we receive more surveys from patient who are in the ED for more that 4 hours. So is the data accurate if the people returning surveys are doing so to file a complaint. To test this theory, I began to give every discharged patient an ED specific survey. This survey mirrored the Press Ganey Format and was ten questions about their ED visit.

If I receive a negative survey, I contact the patient, extend an apology and do some customer recovery. The interesting fact is that when I initiated this policy, I was able to see in impact our Press Ganey statistical data; in those less negative surveys were returned. So in summation, objective statistical information plays a critical role in measuring the quality of the care we provide to patients every day. However, the accuracy of each point of data needs to be weighed and consistency in collection must be maintained. In the end, quality patient care is the priority. Do not let your desire to improve your facility’s statistical place effect that outcome.


Bennett, J. O. , Briggs, W. L. , Triola, M. F. (2009. ) Statistical reasoning for Everyday Life. (3rd Ed. ) Boston, Massachusetts: Pearson Addison Wesley Press Ganey. (2010). Outcomes Driven, Performance Strong. Retrieved November 19, 2010 From the Press Ganey Web Site: http://www. pressganey. com/index. aspx CERTIFICATE OF ORIGINALITY: I certify that the attached paper is my original work and has not previously been submitted by me or anyone else for any class.

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