Three oncology leaders share how AI has centralized care coordination for high-risk patients, leading to tremendous outcomes, both clinically and financially.

Despite notable progress in treatment options, cancer continues to rank as the second leading cause of death in the United States. While we’ve made significant strides in advancing cancer mortality rates, there is still considerable work ahead. One major contributing factor is the delayed diagnosis, nixing the opportunity to cure cancer while it’s still treatable.

Two proven initiatives that are often overlooked that can enhance early cancer detection are screening and incidental findings programs. Regrettably, these initiatives remain poorly adopted by both patients and health systems.

Part of the reason these programs are poorly adopted in health systems is a lack of clear ownership. Screening and incidental findings programs require a lot of coordination, across many service lines, and there often isn’t one central team  responsible for oversight. Additionally, tracking these patients can be incredibly laborious and time consuming.

To better understand how AI is powering stage shift in cancer diagnosis, I sat down with oncology leaders to see how they’ve spearheaded incidental findings programs at their respective health systems.

Incidental findings are abnormalities found by an imaging test that is unrelated to the reason the test was ordered. Nationally, 40% of all scans performed surface an incidental finding but 70% of these will not receive timely and appropriate follow-up.

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