Pravin Pant, MSHI
July 22, 2022
Member engagement has been a crucial component within the healthcare industry, and it is growing in importance. It is believed that an engaged member helps an organization achieve the “triple aim,” which is better care, improved patient satisfaction, and lower costs. Mitchell Morris estimates that healthcare administrative costs total around $200 billion a year in the U.S.
Starting in 2023, CMS put forward key changes to its Star measures criteria. One of the newly proposed measures is on member engagement with more weight than previous years. While this change goes into effect in the year 2023, member surveys have started to evaluate member perception. This will be playing a major role when it comes to the overall score for Star measures.
It is crucial to build an AI member engagement strategy within an organization sooner rather than later. The benefits of using AI and machine learning as part of member engagement include being able to predict the probability of engagement, as well as being able to fully automate the engagement workflow whether through email, text, or member apps. This automation takes the guesswork and potential human error out of implementing member engagement strategies, helps you better understand population- and member- level insights, and makes measuring progress easier.
The value of AI for achieving the triple/quadruple aim is clear, but impediments to adoption may include a lack of data governance and a reluctance to want to automate. To meet and overcome these challenges, ZeOmega has built ready-to-use member engagement models with our own ML/AI technology. Our member engagement probability model not only gives a probability of engagement score but also automates the workflow by offering customizable Jiva UI plugin opportunities for broader use and benefit to our clients. Contact us to learn more about putting AI to work for your organization’s member engagement strategies.