Factors Influencing Best Practices in Treating Multiple Sclerosis: Results from a Predictive Modeling Analysis

Thursday, May 31, 2018
Exhibit Hall A (Nashville Music City Center)
Jamie Reiter, PhD , CME Outfitters, LLC, Bethesda, MD
Jan Perez, BS , CME Outfitters, LLC, Bethesda, MD
Sharon Tordoff, BS , CME Outfitters, LLC, Bethesda, MD
Whitney Faler, MPA , CME Outfitters, LLC, Bethesda, MD

Background: Healthcare providers (HCPs) treating patients with multiple sclerosis (MS) are faced with a continually changing treatment landscape. Establishing effective patient-centric treatment plans, based upon newly identified targets, varying mechanisms of action (MOAs), and different efficacy and safety profiles, will be increasingly essential for optimal patient outcomes. Achieving a balance between remaining current on the latest treatments, customizing treatment, and increasing workloads is an ongoing challenge for HCPs in MS.

Objectives: Use predictive modeling to determine factors that influence HCP practice behaviors to gain a better understanding of barriers preventing best practice implementation.   

Methods: Educational outcomes data were obtained from an educational activity on MS, which consisted of a faculty-led webcast integrating audio recordings of patient feedback, insights and management challenges obtained through interviews. HCP surveys assessing knowledge, confidence, and behavior were administered before, immediately following, and 3 months following the activity. An analysis using PredictCME (based on chi-square automatic interaction detection) was conducted on data from the pre-activity survey, which included 2 behavior questions related to utilizing a point-of-care decision tool and selecting treatments based on MOA and safety profile. Data from these 2 questions were converted to a single behavior score and used as the response variable in the analysis. Demographics, knowledge, confidence, and evaluation data were entered as predictors.

Results: Over 1800 HCPs participated in the activity, with 258 HCPs participating in the pre-survey used for the analysis. Findings revealed confidence to be the strongest predictor of behavior (Χ2(2) = 24.96, p < .001), with those who were less confident being less likely to perform practice behaviors. A secondary predictor, affecting only those who were less confident, was specialty (Χ2(2) = 24.86, p < .001), with neurologists outperforming primary care providers (PCPs) and other specialists. 

Conclusions: Results from the PredictCME analysis suggest that building HCP confidence is an important component of ensuring best practices are performed for the management of patients with MS. While it is expected that neurologists are more likely to perform optimal behaviors than PCPs or other specialists, targeting education to, and building confidence for, both neurologists and non-neurologists is essential.