Practical Application of Statistics in Nursing

Statistics is a fundamental part of human knowledge. It is known to be the exact science of collection, interpretation, analysis and presentation of data. It is a mathematical science that gathers and explains causal phenomenon or relationship, analyzes and presents measurements, collects and analyzes information base on factual sources and presents data as accurate as possible. As Fowler, Chevannes and Jarvis (2002) put it, “Statistics looks at ways of organizing, summarizing and describing quantifiable data, and methods of drawing inferences and generalizing upon them” (p.

1).

The application of statistics in nursing curriculum is important because nurses, like other medical professionals, deal with a variety of information that needs statistical treatment of the data. For example, on their everyday encounter with patients, nurses need to apply statistics to calculate the average number of patients examined per day, week, month or year. Measuring the average number of patient examination would enable nurses to predict as to what month health consultation is at its peak.

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Without knowledge in statistics, nurses would be clueless on what to expect during the incoming month or season.

Another example, when giving medicine to the patient, nurses must be able to determine the time interval when a patient should take the prescribed medicine. Nurses, too, must be able to identify what percentage of the admitted patients are carriers of transferable diseases, victims of chronic diseases and others. Moreover, application of statistics such as econometric statistical techniques is helpful in analyzing the cause-effect relationships of diseases and the severity of diseases through correlation and regression principles.

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History of Statistics Application in Nursing The demand for statistical literacy among nurses was brought by the growing importance of nursing researches which started on Florence Nightingale’s “Notes of Nursing” published in 1859. Nightingale was able to describe the factors that affected the performance of soldiers who were involved in the Crimean war. She was able to determine that other illness contracted off the field of war and caused unattended wounds. Nightingale’s notes then became the basis for a review of what and how much amount of care the soldiers should receive (Lipsey, 1993).

Following Nightingale’s notes, nursing research became an important part of the nursing curriculum which led to the establishment of the American Journal of Nursing. This journal started to publish nursing research studies as early as 1930s. In 1970s, the focus of research was on the investigation of nursing practice and the outcomes of nursing. This required an in-depth knowledge of research design and statistical methods where clinical problems and issues related to nursing practices were subject to investigation. Uses of Statistics in Nursing

In evaluating the use of statistics in nursing, Fowler, Chevannes and Jarvis (2002) identified two reasons why nurses need to be statistically equipped. One of the reason is that statistical literacy is required if nurses are to read and evaluate critical and intelligent data, reports and other literature related to managing health care. Another reason is that knowledge in statistics would help nurses whenever they would decide to undertake an investigation that includes the collection, processing, interpretation and analysis of data and reports on their own account.

However, statistics have limitations; it does not prove anything, instead it presents the likelihood of the things to happen based on the result of an investigation (Fowler, Chevannes and Jarvis, 2002). Statistics and the Health Care Investigation Health Care investigation generally involves a five-stage process: 1) identifying the problems and objective; 2) planning; 3) collection of data; 4) interpretation and analysis of the data; and 5) presenting and reporting the result of investigation. In doing health care investigation, the following methodologies are usually utilized: sample survey, clinical trials and epidemiological studies.

Usually, getting a sample population is needed to conduct such methodology. In statistics, population means a group or collection of individuals who are the subject of investigation. Variables entails the different characteristics of individuals such as age, weight, height, number of heart beat, manifestation of symptoms and economic status which relate to the health condition of individual. Since it is hard to get all the population to be involved in health care investigation, nurses may use smaller groups or sub-set who will represent the group as a whole. This group is known as sample.

Each individual or unit in the sample can provide a data like measurement. This record is called an observation (Fowler, Chevannes and Jarvis, 2002). For example, the nurse wanted to find out whether Generation Y babies (which is the sample unit) are malnourished or just on average weight. The nurse must first identify the duration of the investigation, number of babies to be investigated, and the age and gender of the infants. This would be the variables. Observations would be based on the measurement of each sample infant included in the health investigation.

Explanatory Nursing Studies and Inferential Statistics Researches which aim to explain the elucidate the relationship among the variables are more complex than other descriptive studies. Lines of inquiry for this study are often based on establishing theories from other research literature. Example of questions could be: Are people born with mental disease more likely to survive Post Traumatic Syndrome Disorder than people with chronic diseases? Are chronically ill patients more likely to improved under the care of nursing home than on the care of their family?

In explanatory study, are not being investigated based on the cause-effect relationship rather it attempt to understand how the given variables are related to each other. Thus, inferential statistics are utilized to analyze or explanatory elucidate the relationship of the variables (Plichta and Garzon, 2009). Prediction and Control Nursing Studies and Statistics Statistics, like medicine, is also an important tool to prevent and control diseases. In prediction and control studies, nurses aim to determine which variables are able to determine causality and are predictive.

Such studies are usually quasi-experimental whereas the researcher is bound to introduce an intervention. Experimental designs involve random selection, an intervention, two different groups – one group that receive an intervention and another group that do not receive an intervention – and random assignment of the study participants to either the intervention or the control group. Like explanatory studies, prediction and control studies uses inferential statistics to examine the data and provide answer to the research questions (Plichta and Garzon, 2009).

The Statistical Analysis of Health Data Without the use of statistics, it will be hard to identify which diseases or ill conditions are critical or not. On any instance, medical professionals, particularly the nurses, rely on the result of clinically proven studies on attending to the needs of their patients. Nurses need not only rely on doctor’s order but also they need to be well equipped when dealing with patients. Most of the time, nurses are the primary person who attend to emergency situation. Thus, they should also be knowledgeable in analyzing their patients’ condition based on the medical variables related on the illness or disease being experienced by the patient.

However, there are studies with similar topic but posses different results. For example, there are studies that prove the effectiveness of applying chemotherapy to cancer patients. On the other hand, there are studies that show how doctors negatively react on the use of chemotherapy to cure cancer patients. For nurses to avoid being trapped on ethical dilemmas, they must know how to critically analyze those studies that are of their concern, particularly those with statistical measurements. Research studies with statistical analysis of the data usually undertake three stages.

First, the data should be cleaned. By cleaning the data, it means that all the variables in the study must have valid and usable values. Running frequencies and examining these frequencies must be done for the researcher to be able to identify the valid values, the amount of missing data and the adequate variability. Nurses, when reading and analyzing such data, must also be able to examine the frequencies used by medical researchers. They should be able to identify the missing data and the validity of the values presented in the studies. The second step in statistical analysis is describing the sample.

Here, the researcher employs descriptive statistics with a table that displays the sample’s characteristics are presented. For example, the medical researcher uses a graph, a chart or a table to present the sociodemographic characteristics (e. g. age, weight, height and gender) of the sample patients. Such description enables not only the medical professionals but also the common people to understand the sample population involved in the study. Moreover, in describing the statistical analysis, the key dependent and independent variables are given enough presentation.

Nurses must be able to categorize which variables are independent and which are dependent. The last step in statistical analysis of health data is to be able to identify the list of inferential statistics that will be used to test the hypotheses. Application of inferential statistics depends on the research design, size of the sample and the distribution of variables (normal vs. non-normal), scale measurement of the variables (ratio, nominal, interval, ordinal) in the hypothesis and the type of comparison that needs to be made.

It is also important to note that for small sample population and for variables which are not normally distributed, nonparametric statistics are used while for large sample population and for normally distributed variables, parametric statistics are utilized (Polit, 1996). In order to not to be deceived by the validity of the hypothesis presented by medical professionals, nurses must be knowledgeable on the application of statistics and its variation. Generally, nurses of today’s generation must be well statistically equipped.

They should be able to distinguish which studies are applicable on their field of practice and on the nature of their workplace or community. Not all nurses are working with advanced health facilities thus they should be able to manually learn the basic principles of statistics which will be helpful on every part of their duty – from giving medicine to their patient to reporting the results of treatment, generating research studies, predicting and controlling diseases and applying the fundamental results of valid health studies.

Without knowledge in statistics, nurses would fail to give enough care and treatment needed by the patient. Moreover, without knowledge in statistics, nurses, who works directly and personally with their patients, would fail to understand the different conditions being experienced by the patients. With the aid of statistics, nurses would be able to contribute to the growing number of medical researches particularly those which relate to the vocation of nursing.

Abstract

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 Statistics

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

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.

References

1. Levels of Measurement. (n.d.). Retrieved January 23, 2015, from http://onlinestatbook.com/2/introduction/levels_of_measurement.html
2. 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