Positive Feedback Received for ACEhp Webinar on Predictive Modeling Conducted by CME Outfitters’ Director of Educational Outcomes

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Director of Educational Outcomes for CME Outfitters, Jamie Reiter, PhD, Shares Basic Methodology for Predictive Modeling via ACEhp Webinar

Congratulations to our own Jamie Reiter, PhD, Director of Educational Outcomes at CME Outfitters (CMEO), who was invited by the Alliance for Continuing Education for the Health Professions (ACEhp) to lead a webinar on predictive modeling. Entitled, “Understanding the ‘Why’: Using Predictive Modeling to Inform Outcomes,” the webinar was held on April 13, 2017, and is currently available on-demand. It focused on providing medical education professionals with information on the importance of understanding factors influencing activity success, as well as the skills to conduct their own predictive modeling analysis.

A copy of the course description and learning objectives is provided below.

Description: In medical education, we frequently ask whether or not an activity was successful, that is, whether it improved outcomes. But rarely do we ask why. If an activity was successful, do we know the formula to ensure this success continues in future activities? Conversely, if an activity was less than successful, do we know which barriers prevented improvement? Factors such as learner demographics, activity format, therapeutic area, question wording, knowledge, and confidence can influence responses to behavior questions or other endpoints. Being able to answer the “why” related to educational outcomes success is an important component of developing activities that ensure best practices are being implemented, resulting in improved patient outcomes.

There are two main approaches to understanding the factors influencing outcomes. The first is to use traditional statistical analysis to compare subgroups of participants (e.g., primary care physicians and neurologists), separating them by variables suspected of influencing outcomes. This approach comes with its share of challenges, primarily: 1) How do we decide which variables to use, and 2) Exploring all possible subgroups from all possible variables can be cumbersome. The second approach is to use predictive modeling, the most common method of which is regression.

The medical education industry is starting to appreciate the value in predictive modeling, but many providers may not feel they have the skills to perform predictive modeling or don’t realize the software is readily available. Statistical analysis is typically best accomplished by a knowledgeable statistician using software specifically designed for statistics (e.g., SAS, SPSS). However, with caution, basic statistical procedures can be conducted by non-experts who have some degree of statistical knowledge, and using software such as Excel or online calculators.

This presentation will provide an overview of predictive modeling and provide an example of how to conduct regression using Excel as well as a few vetted online calculators. It is not the intention of this presentation to provide a full understanding of the mathematics and statistical theory behind predictive modeling, rather a basic overview. Some knowledge of, or at least comfort with, statistics is recommended.

Learning Objectives:

Compare/Differentiate standard statistical methods and predictive modeling for gaining insight into educational activity success.
Obtain skills for conducting a predictive modeling analysis.

“I began using predictive modeling at CME LLC back in 2008 and saw its value in medical education. I appreciate that the ACEhp gives industry professionals the opportunity to share best practices with one another to help elevate the industry and ultimately benefit patients,” said Dr. Reiter. “As statistics can often be intimidating, my intention for this webinar was to provide a ‘friendly’ introduction to predictive modeling while providing step-by-step instructions on how to conduct the analyses, by providing a foundation using the most basic (linear regression), then easing into the more complex, but more applicable for our industry (logistic regression). Of course there are other methods for predictive modeling such as multinomial logistic regression, naïve Bayes, and CHAID [via PredictCME, which is CMEO’s unique offering for applying CHAID to CME outcomes], and I hope to be able to continue to share this information with our community.”

Reaction to the webinar was positive. Stated Jason Olivieri, MPH, Director of Outcomes at Med-IQ, “That was a great webinar, thank you.  By that I mean, you provided a very clear recipe for others to elevate their assessments (or at least better understand data beyond pre vs post).  I wish such guidance was more common in our industry.” Other comments included: “This was awesome, thanks! But let's do a part two that explores other models, please,” “Excellent handouts and slides. Practical information to use in practice,” and “The information and examples she provided were very informative.”

The on-demand version is available at: http://www.acehp.org/p/pr/vi/prodid=55.

About CME Outfitters, LLC
CME Outfitters develops and distributes live, recorded and web-based, outcomes- and evidence-based educational activities to thousands of clinicians each year and offers expert accreditation and outcome services for non-accredited organizations. CME Outfitters focuses on delivering education to specialty audiences, with strong expertise in neuroscience, inflammatory, infectious, and autoimmune diseases, and cardiovascular disease. For a complete list of certified activities and more information, visit http://www.cmeoutfitters.com or call 877.CME.PROS (877.263.7767).

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Improving Clinical Behavior … One Change at a Time”

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Anna Larkin
CME Outfitters LLC
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