A health plan in the southeast recently undertook an effort to optimize its outreach strategy with the help of advanced analytics and machine learning. Prior to this initiative, the plan's outreach efforts were largely driven by a reactive approach, where staff would contact members based on a predetermined schedule, without much consideration for the individual member's needs or circumstances.
However, as the plan began to leverage ML-driven outreach, they started to gain a more nuanced understanding of their member population. By analyzing member behavior, demographic data, and other factors, the plan's outreach efforts became more targeted and strategic.
One example of this shift is the plan's approach to outreach to members with diabetes. Rather than trying to contact all members with diabetes at the same time, the plan's ML system began to identify those members who were most likely to benefit from proactive outreach. This might include members who had recently been hospitalized for diabetes-related complications, or those who had been experiencing difficulties managing their blood glucose levels.
With this more targeted approach, the plan saw a significant reduction in wasted outreach efforts, as members were only receiving calls when they were most likely to engage with them. Furthermore, the plan began to notice a marked increase in the number of members closing care gaps and improving their outcomes. This was particularly evident in the area of medication adherence, as members were being proactively contacted to ensure they were taking their medications as prescribed.
Perhaps most striking, however, was the qualitative shift in member perception and engagement. Members began to view the plan's outreach efforts as more personalized and supportive, rather than simply another annoying phone call. This shift in perception not only improved member satisfaction, but also helped to build trust and loyalty with the plan, ultimately driving better health outcomes and more efficient care delivery.
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