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Displaying patterns and trends of healthcare data can really help in optimizing the quality of service.
The massive data generated by the healthcare sector can generate huge bits of insights into whether quality assurance-oriented metrics are growing or declining. You can easily know whether key data points are down or up-moving.
The tested and proven chart for displaying general patterns and trends in your healthcare data is the Control Chart.
The visualization design (i.e., Control Chart) is amazingly easy to interpret. Plus, you can utilize this graph to make convincing data stories.
Excel has very difficult process of creating Control Chart.
However, you don’t need to get rid of the spreadsheet app. You can get to an instant and ready-made Control Chart by introducing a specific add-in in your Excel.
In this blog post, you’ll learn the following:
Before jumping right into the how-to guide, we’ll address the following question: what is a Control Chart?
The Control Chart is a graph you can use to investigate how a process changes over time.
The diagram has a focal line for the normal, an upper line for the upper control limit, and a lower line for the lower control limit.
By contrasting your data with these lines, you can easily conclude whether the process variation is steady or volatile and impacted by special causes of variation.
This chart can be used in the healthcare sector and is viewed as one of the seven fundamental quality control devices. Also, the chart is best used in pairs.
What do we mean?
The top chart displays the average or the centering of the distribution of data of a process. The other chart shows the reach or the width of the data sample understudy.
So, what are the origins of the chart?
The Control Diagram was first coined by Walter A. Shewhart, working for Bell Labs during the 1920s.
The organization’s engineers had been pursuing to enhance the consistency and reliability of their telephony transmission system. Since amplifiers and other equipment had to be covered underground, there was an urgent need to decrease the recurrence of disappointments and frequent fixes.
The engineers understood that consistent cycle change in response to non-conformance expanded variety and debased quality. Shewhart outlined the problem in terms of common and special causes of variation.
During the 1920s, Shewhart composed an internal memo introducing the Control Chart as an instrument for probing future problems associated with telephone transmission.
The main elements of a Control Chart include:
So, when should you use a Control Chart?
So, what are the uses of Control Charts in the healthcare industry?
The Control Chart in healthcare is incredibly easy to read and interpret. Besides, the visualization design is incredibly easy to generate if you have the right tool.
You can leverage this chart to make decisions, especially in the healthcare industry.
If a particular process is in control, all the points will fall between the control limits. Any observations outside the limits, or systematic patterns within, suggest the introduction of a new source of variation, known as a special-cause variation.
Since increased variation means increased quality costs, a Control Chart flags the presence of a special cause that requires immediate probing in your healthcare institution.
This makes the control limit lines very important decision aids. Also, they provide key insights into the process behavior.
The key goal of a Control Chart in healthcare is to display insights into key data points with the view of optimizing the process flow.
Besides, it can help you find answers to the following:
Keep in mind that our brains process graphs and charts multiple times quicker than poring over spreadsheets and tables.
A Control Chart is a quick and easy way to convey insights to a broader audience. Make the use of the chart a habit in your healthcare organization to enjoy the benefits below:
A Control Chart in healthcare makes it amazingly easy to extract answers from your data to create compelling stories.
Imagine using the tables and spreadsheets to make sense of insights and other critical bits of knowledge about healthcare.
What are the chances of getting a quick buy-in of your ideas?
Our minds handle visual content, for example, charts and graphs, multiple times quicker than tables.
A compelling data story stacked with simple to-decipher Control Charts can boost quicker decision-making.
Visualizing data can help the healthcare industry to extract actionable insights.
And this implies the industry can pinpoint connections and patterns between metrics. Exploring these patterns empowers you to save tremendous assets, like time, by zeroing in just on regions that need earnest action.
A Control Chart in healthcare can assist you with effectively spotting mistakes in your data. Working with mistake-free data help in improving the reliability and credibility of the insights generated.
We visualize data to make data stories. Poring over numbers in tables is time-intensive and boring.
So, you want to make a convincing story with bits of insights extracted from the crude data. Individuals love stories and tales because they appeal to feelings and emotions.
To make a convincing data story, you want a real story.
It sounds disconnected. Yes, we know.
To make a story, begin by posing an inquiry or shaping a theory. And afterward, dive into your data to track down hidden answers.
Below are some of the questions you need to ask:
Remember, a Control Chart in healthcare comes before you create a story.
In the coming section, we’ll take you through how to make a healthcare Control Chart in Excel.
Excel is a trusted data visualization tool because it’s familiar to many. But the spreadsheet application have very difficult process of creating Control Chart.
We understand switching tools is not an easy task.
Therefore, we’re not advocating you ditch Excel in favor of other expensive data visualization tools.
There’s an easy-to-use and amazingly affordable visualization tool that comes as an add-in you can easily install in your app to access a ready-made Control Chart. The tool is called ChartExpo.
So, what is ChartExpo?
ChartExpo is an incredibly intuitive add-in you can easily install in your Excel without watching hours of YouTube tutorials.
With many ready-to-go visualizations, the Control Chart generator turns your complex, raw data into compelling, easy-to-interpret, and visually appealing excel charts that tell data stories in real-time.
More benefits
In the coming section, we’ll take you through how to visualize data using a Control Chart using the ChartExpo add-in.
You don’t want to miss this!
This section will use a Control Chart in healthcare to visualize the tabular data below.
You don’t want to miss this.
| Months | Number of Complaints |
| January | 9 |
| February | 12 |
| March | 12 |
| April | 8 |
| May | 14 |
| June | 12 |
| July | 14 |
| August | 12 |
| September | 12 |
| October | 11 |
| November | 11 |
| December | 10 |
To install ChartExpo into your Excel, click the following link.
The Control Chart in healthcare is a graph you can use to investigate how a process changes over time. Data in this visualization design is plotted across time.
The chart has a central line for the average, an upper line for the upper control limit, and a lower line for the lower control limit.
A Control Chart in healthcare makes it amazingly easy to extract answers from your data to create compelling stories.
Imagine using the tables and spreadsheets to explain emerging patterns and other significant insights about a hospital to your audience.
You can leverage this chart to make reliable decisions to improve service quality.
Visualizing trends in healthcare data is one of the critical tasks you can ever undertake.
Why?
It provides significant insights into whether key metrics are on a growth or decline path regarding service delivery. You can easily know whether key data points are down or up-trending.
The tested and proven visualization design for visualizing general trends in your data is the Control Chart.
The visualization design (mentioned above) is amazingly easy to interpret. Besides, you can use this chart to create compelling data stories.
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