Trend analysis involves examining data over time to identify patterns, trends, and fluctuations. One common technique for this is using a moving average. A moving average smooths out the data by calculating the average of a certain number of consecutive data points. This helps to visualize the underlying trend by reducing noise and highlighting patterns.
Here’s how you can perform trend analysis using a moving average on patient results, along with an example:
Step 1: Gather Data
Collect your patient results data over a specific period of time. Ensure that you have a sufficient amount of data points to observe trends
Step 2: Choose a Window Size
Determine the size of the moving average window. This is the number of consecutive data points you’ll use to calculate the average. A larger window provides a smoother trend line, but it might obscure short-term fluctuations.
Step 3: Calculate the Moving Average For each data point, calculate the average of the values within the chosen window size. As you move through the data, the window “slides” along, incorporating new data points and dropping old ones.
Step 4: Plot the Data
Plot both the original patient results and the calculated moving averages on a graph. This will help you visualize the trends more easily.
Example: Blood Glucose Levels Let’s say you’re analyzing blood glucose levels for a diabetic patient. You have daily readings over the past 30 days. You want to use a 7-day moving average to analyze the trend
- Gather Data: Collect the patient’s blood glucose readings over 30 days
- Choose a Window Size: In this case, we’ll use a 7-day moving average
- Calculate the Moving Average: For each day, calculate the average of the blood glucose readings for the current day and the previous 6 days.
- Plot the Data: Create a line graph with the original daily blood glucose readings as well as the calculated 7-day moving average.
Here’s a simplified example of how the data might look:
| Day | Blood Glucose Reading | 7 Day moving Average |
| 1 | 150 | |
| 2 | 145 | |
| 3 | 160 | |
| ….. | …… | |
| 30 | 155 |
By calculating the 7-day moving average, your graph will show a smoother line that gives a better sense of the overall trend in blood glucose levels, removing some of the day-to-day fluctuations.
Remember that the choice of window size can impact the level of detail you see in the trend analysis. Smaller window sizes capture short-term fluctuations, while larger window sizes reveal longer-term trends.
About the author
Dr. Sambhu Chakraborty is a distinguished consultant in quality accreditation for laboratories and hospitals. With a leadership portfolio that includes directorial roles in two laboratory organizations and a consulting firm, as well as chairmanship in a prominent laboratory organization, Dr. Chakraborty is a respected voice in the field. For further engagement or inquiries, Dr. Chakraborty can be contacted through email at director@iaqmconsultants.com and info@sambhuchakraborty.com. Additional resourcesand contact information are available on his websites, https://www.quality-pathshala.com and https://www.sambhuchakraborty.com, or via WhatsApp at +919830051583