What is in this article?:
- Demonstrating the Economic Impact of CME
- Cost Savings From a CME Activity
Medical education company CMEology has developed a computer model that is designed to tackle a pressing problem in continuing medical education: Proving that it can help produce savings in patient care.
Increasingly, stakeholders throughout the healthcare system are challenging educators, continuing medical education providers, and commercial supporters to prove that CME lowers healthcare costs and benefits society. Until recently though, it has been difficult to prove the actual dollar value attached to CME outcomes. Our new computer model is changing that. According to a recent study we conducted, CME can lead to millions in healthcare savings, even when only a modest number of participants change their behavior as a result of the activity.
Where’s the Data?
There have been very few studies on the economic impact of CME, which is remarkable considering that, according to the Accreditation Council of CME’s 2012 Annual Report, more than $2 billion is spent annually on accredited CME. One possible reason may be that collecting and analyzing patient-level economic data is cost-prohibitive, time-consuming, labor-intensive—and often unrealistic given shrinking CME budgets.
Recognizing that economic outcomes should be reported routinely, CMEology researched ways to measure cost savings associated with CME. The goal was to bring economic measures within reach while meeting the high standards expected for outcomes measures and policy decision-making.
The CMEology team turned to existing health economic models that have served as the basis for policy making and expenditure allocations. Adapting these approaches to the needs of CME, the group developed a computer model. The approach, called outcomes impact analysis, or OIA, estimates costs averted when CME activity participants return to their practices and apply what they learned to change the health outcomes of their patients. Using state-of-the-art statistical and probability methodology, the mathematical model provides a reliable estimate of cost savings. One of the strengths of OIA is that it uses probabilistic sensitivity analysis that simultaneously considers the impact of all the real-life ranges of the different variables in the model. The result is a robust range of the estimated cost savings within which the true value is highly likely to fall.