Session Provides Tips for Helping Patients Organize their Data
Alison Evert, MS, RD, CDE
The glut of information available to diabetes educators can sometimes be challenging. Blood glucose data may be scribbled on a sheet of paper. Patients may be not understand why their dietitian or educator needs to see their monitor.
Alison Evert, MS, RD, CDE, a diabetes educator with 37 years of experience, has pretty much seen it all and is ready to help other educators better prepare their patients for their clinical visits. She will present “Making Sense of Glucose Data – Meters and CGM” from 3:15 pm to 4:15 pm today in Room 130-133.
“We are all limited with how much time we have with our patients. The goal of my session will be helping educators ready their patients for sessions by maximizing the organization of the information upfront so they can really roll up their sleeves when they are with that patients,” said Evert, who is Manager of the Nutrition and Diabetes Program for the University of Washington Neighborhood Clinics.
She will describe how to evaluate glucose data from glucose meters and continuous glucose monitoring (CGM) reports, cover the pros and cons of using personal or professional CGM, and discuss how to evaluate glucose data from a case scenario to formulate treatment recommendations.
“I’ll be providing what I call practice pearls for when you only have access to blood glucose monitoring. What can you do as an educator to help your patient organize their data, using handwritten records to provide you with as much information as possible?” she said.
Using case studies to illustrate her message, she’ll look at self glucose monitoring data reports and give tips for interpreting the data from different types of continuous glucose sensors.
“I hope the discussion gives educators more confidence in their ability to evaluate glucose data from meters and CGMs,” she said. “While I won’t be going into detail about the new hybrid closed loop technology, I will be giving more general tips for evaluating retrospective data analysis.”
