For this assignment, I will be looking at the different uses of statistical information used in the healthcare setting. Statistics in healthcare are used in many areas including human resources, employee retention, patient satisfaction, and too many others to name. Hospitals use this information to look at areas that need improvement, to save money, and to improve different workflows. In today’s healthcare environment, it is much easier to look at these statistics with the implementation of the electronic medical record. Statistics that would have taken days or even weeks to obtain can now be filtered through a computer and retrieved in a matter of minutes or seconds.
Statistics Used In the Workplace
Statistics measure a wide variety of items in the workplace. I will focus on those typically used in the emergency department. One statistic used is for stroke patients. It measures the length of time it takes from arrival at a community hospital to the time they are transported to a hospital with a higher level of care. Another statistic that my hospital measures is the door to discharge time. This measures the amount of time it takes for diagnosis, treatment and discharge. Hospitals monitor this closely because insurance companies only allow patients to stay a certain number of days depending on the diagnosis.
The patient stays longer than what the insurance company will pay for, the hospital is losing money. The last statistic but I will talk about that is measured in my hospital – patient satisfaction. This statistic is collected and compiled by CMS (Center for Medicaid and Medicare Services). The survey asks approximately 20 questions to a patient has been discharged. These questions are regarding the care that they received, cleanliness of the rooms, if the call light was answered in a timely fashion and so on. These scores are then placed on a website for the general public to view describing how each hospital scores compared to another hospital in that area or across the country.
Descriptive statics looks at similarities and summarizes them. It is used to look at raw data using graphics and sample statistics. An example of descriptive statistics used would be that of looking at what patients come through the emergency department with a diagnosis of CHF, heart failure or respiratory distress. Hospital and nursing administrators are looking at finding the mean of all patients that came through the emergency room over a given time frame in relation to all the patients that were seen in the emergency room. This allows administration to have a better understanding of what the needs of the emergency room to better care for this patient population in relation to time of year and other factors that are obtained in this data.
Inferential statistics infers or estimates population parameters from sample data. An example of inferential statistics is measuring visitor satisfaction. A random sample of visitors not patients are not a patient was asked a few simple and easy questions. A random sample was used because it would be impossible to sample every visitor that came into the hospital. Sample questions could include, “Could you find where you needed to go without difficulty”? Another example would be were you treated well by the receptionist? Inferential statistics are able to summarize the data showing what areas the hospital needs to work on to increase visitor satisfaction.
Four Levels of Measurement
In my workplace, the four levels of measurement are used frequently. The first measure is Nominal Scales. With every patient that comes to the hospital, we measure certain things such as gender, religion, date of birth, height, and so on. After collecting this data, you can filter patients according to their gender religion the month that they were born etc. This filter allows the hospital to look at the different populations, average weight of our patients, and the average height of our patients. It may not seem significant but having the right type of bed and enough of those beds is extremely important in the hospital setting. Nominal Scales can provide that type of necessary information. The second level of measurement is Ordinal Scales. Ordinal Scale measurement would be that used to measure patient satisfaction.
CMS sends out a survey that measures the patient’s satisfaction during their hospital stay. The answers to each of the survey questions are never, sometimes, usually and always. Different from nominal scales, ordinal scales allow for comparisons which two subjects possess an independent variable. One thing to remember is that ordinal scales do not capture important information that is normally present in some of the other scales. The third level of measurement is Interval Scales.
These are number scales in which the interval has the same interpretation throughout. In healthcare, an example of this would be a blood pressure cuff or a thermometer both have scaled or equal measurements. Let’s look at a blood pressure cuff. Each mark indicates 2 mmHg so when you take a blood pressure and you pump it up and listen and obtain a blood pressure of 110/80. You can compare that to another patient using the same blood pressure cuff to obtain blood pressure that may be 170/110. Since the blood pressure cuff has equal intervals, you know that there should be no variation in what the dial reads other than that of the patient.
The fourth level of measurement is Ratio Scales. This measurement scale is the most informational scale. It is in interval scale with the addition of the property that its’ zero position shows that there is no quantity being measured. The scale can be used in healthcare when determining the amount of money coming into the hospital in regards to the money going out of the hospital. The ratio of those two allow the hospital to operate and look at where money needs to be saved or spent.
Advantages of Accurate Interpretation of Statistical Information Without accurate interpretation of the statistics used in the healthcare setting, there would be problems in many areas of the hospital. These problems would occur in a variety of areas including staffing problems, employee satisfaction rates, patient satisfaction rates, visitor satisfaction rates, patients developing infections, poor money management, plus many more. All of these areas are measured and looked at with statistical analysis. Knowing how to interpret this information accurately allows managers to make informed decisions about the organization. We are fortunate as a society to have computers and electronic medical records to help accurately interpret these statistics in the healthcare setting. Accurate statistics and statistical analysis are vital to healthcare in 2015 and beyond.
Levels of Measurement. (n.d.). Retrieved January 23, 2015, from http://onlinestatbook.com/2/introduction/levels_of_measurement.html Bennett, J. O., Briggs, W. L., & Triola, M. F. (2009). Statistical Reasoning for Everyday Life (3rd ed.). Retrieved from The University of Phoenix eBook Collection Database