Say you have a tabulation of hemoglobin values of 2000 people and you need to find the average value of the same or the most common value; you may need to employ central tendency concepts like mean, median, or mode. Not only in such scenarios, these notions also have their applications in various health care departments.

Being employed for years now, it was known by fewer professionals. In this post, we will look into mean, median, and mode~~ ~~and their applications in the healthcare industry.

**Mean, median and mode- Crucial concepts in statistics**

Having an average of data can often drive conclusions faster. When the data is enormous, determining averages becomes mandatory. Being aware of the same, individuals can use notions like mean, median, and mode to determine desired results.

**Mean **is that value lying intermediate among the extreme entries of the data. In simple terms, Mean is nothing but the average of the set of data available. This can be best used in areas where the number in the set are very close together. To find the mean of numbers X1, X2, X3….XN is denoted mathematically as :

**Arithmetic mean (X)=(X1+X2+X3…..+XN)/n**

Where, n= Number of entries in the set.

**Median** is the middle one or the average of the middle numbers of the set when arranged in ascending or descending order. In layman’s terms, Median is the middle value of the dataset. This turns handy when the distributions are skewed and a central value is to be determined. Accordingly, it is often used to measure central tendency in uneven values like medicines consumed and income earned. This notion is used to determine the middlemen. For instance, the median of the set {1,2,3,4,5} is 3.

**Mode **is the number of the data set which is most frequently repeated. In other words, it is the number that has the highest frequency in the data set. When the data is categorical, employing mode turns obligatory. Say, one considers a set {1,5,4,5,7,5,7,6,2}, the model is 5 since it has the highest frequency(3). Being the measure of frequency, the model can also be employed for shapes and colors too. For example, among 10 balls (1 green, 3 white, 5red, and 1 purple), the mode would be 5 (red).

Comprehending what these central tendency notions are can make you grasp how they are applicable in the healthcare industry like Hospitals, research, and nursing. We shall explore them in detail in the following sections.

**Central tendency in hospitals- Applications to discern**

Hospitals form a pivotal section of the health care department. Being a place to offer medical services to civilians, hospitals and advanced clinics employ mean, median, and mode in areas like :

**1. Service planning**

Health service planning is a new trend in popular hospitals. By definition, service planning is the process of creating actions to take health service to a future improved state. The same is ensured by observing the changing trends and health patterns of the population on the whole. Statistical concepts like median, mean, and mode are necessary to assimilate and evaluate the data. May it be evaluated at the state or national level. These notions turn crucial to observing the change in trends.

For instance, the mode of past five-year data of heart attack can be compared with the present year’s value to mark the change of such cases. Albert Ahenkan^{[1]} made a research to determine the patient satisfaction in the healthcare quality by comparing two hospitals. He used descriptive statistics (encompassing central tendency concepts) to get appropriate inferences.

**2. Resource allocation**

As the name depicts, Resource allocation is the procedure of assigning and managing assets in a way to cope with the hospital’s strategic long-term and short-term goals. An appropriate supply of medicines and other equipment to various departments is necessary to retain the successful operation of the institute. While the allocation department has a list of resources to be allocated to different sections, they often need to determine the mode of the past supplies.

For instance, if the mode of the past supplies determines the need for 3000 syringes a month, they can procure the same for the coming month. Moreover, by doing this, they can also track the change in consumption and thus the reason behind changes in the organization.

**3. Bed utilization**

One of the key components of in-patient care hospitals is making the best utilization of beds. Managing the relevant data to empty and to-be-empty beds can make organizing facile. Some hospitals may have multiple sorts of beds ranging from ordinary to premium. Managing these also turns effortless. Evaluating the average number of beds occupied and are left vacant brings notions like mean, median, and mode to the limelight. The administrative staff, after acquiring these relevant data, can determine how many beds are still vacant and the probable reason behind the same. This way, they can fabricate relevant solutions to resolve such issues.

**Statistical averages in medical research- Obligatory notion?**

**Statistical averages in medical research- Obligatory notion?**

The role of statistics in medical research begins right from the planning stage with the clinical trial or laboratory experiment. Be it establishing the design and size of the experiment or detecting the effects of the research, statistics get crucial everywhere. Evidently, analysis of acquired data obligates concepts like Mean, median, and mode. All this brought out the scope of collaboration between statisticians and medical professions.

The implementation of statistical notions like median and mode in medical research has been in practice now for about 150 years. Florence Nightingale, being a member of the Royal statistical society, started to develop methods to show mortality rates in tabulated formats. Later their analysis needed mean, median, and mode.

Applications in medical research can be often incalculable. Research by Parampreet Kaur^{[2]} remarks that descriptive statistics (like central tendency) can organize manner to describe the relationship between variable and population. The simpler inferences enable healthcare decision-makers to manage future processes. Here we explore a few areas where these concepts come in handy:

**1. Assessment **

Research not only implies clinical research in the labs but also analysis of the population for inferences. In such scenarios, statistical analysis becomes a crucial component. For instance, a median of the entire population can be used to determine the age from which vaccines are to be offered initially. Further, assessing the results of the same also obligates concepts like mean, median, and mode.

**2. Quality Improvement**

Health care providers often look to provide patients with quality and effective products; However, this can be ensured by effective analysis of customer behavior, results, and purchase patterns. Taking a central value and analyzing them can lead the makers to retain benchmarks and service excellence.

Even in Pharmacological research^{[3]}, these concepts come handy in assimilating experimental data and hypothesis testing.

**Mean, median and mode in nursing**

**Mean, median and mode in nursing**

A Nurse’s duty is often more than just assisting doctors in surgeries and treatment. Be it recording the bed hours or calculating the dosages of medicines offered to patients, nurses have a set of duties. Apart from assisting doctors in the treatment, they may also need to assist the administrative department in determining and evaluating figures like the number of beds, average bed days of patients in ICU, compensations of staff, and other data like regular measurement of patient’s temperature.

Different scenarios obligate the implementation of distinct central tendency concepts. For instance, if there is more than one set of populations with varying attributes, evidently mean cannot be employed to determine generalized manifestations. In such cases, notions like mode or median may be used to determine exaggerated inferences. Being aware of these duties, nursing aspirants are mandated to study math in their training.

**Concluding thoughts**

The past century has witnessed many fascinating crossovers of concepts. Many industries started demanding statisticians for analysis of data and thus arriving at probable inferences. In healthcare, the data and its analysis can be yet more crucial since the inferences are related to health. Future scope of statistical approaches can advance services in forensic, epidemiology, and other departments too. Fascinatingly, learning statistical concepts like central tendency is often not taxing. This made such notions obligatory in various training programs facilitating the professionals themselves to infer sets of data effortlessly.

**References:**

*Predictors of Patient Satisfaction With Quality of Healthcare in University Hospitals in Ghana*. (2017). Albert Ahenkan. https://doi.org/10.15171/HPR.2017.03*BIOSTATISTICS*. (2018). Parampreet Kaur.*Statistics in Pharmacology*. (2007, October). D Spina. https://doi.org/10.1038/sj.bjp.0707371