A key takeaway from CMS Star Ratings data analysis is that the vast majority of health plans spend the majority of their outreach efforts on members who are already receiving care and are likely to already meet quality measures. In turn, these plans often overlook the small percentage of members who are most in need and most likely to benefit from targeted outreach.
This issue is particularly relevant when it comes to closing care gaps, such as uncontrolled diabetes or hypertension. Plans that use AI and machine-learning to prioritize outreach efforts can identify members who are at high risk of adverse outcomes and target them with timely and effective interventions. By concentrating outreach efforts on these members, plans can meaningfully reduce wasted outreach and achieve a higher return on investment for their outreach programs.
In concrete terms, this means that plans that implement AI-driven outreach strategies are able to identify and close care gaps for a far larger percentage of their members than those that rely on more traditional, list-everyone outreach approaches. Furthermore, by improving outcomes and reducing costs for these high-risk members, AI-driven outreach can also improve overall quality and financial performance.
Ultimately, the secret to moving CMS Star Ratings is not about finding the exact cut points or weights assigned to each measure, but about understanding the underlying dynamics that drive quality and outcomes. By targeting the right members with the right interventions at the right time, plans can create a virtuous cycle of improved health and better performance on quality measures.
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