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How Cone Health used predictive analytics to increase infusion center volumes by 8% while decreasing wait times by 25%

By In Collaboration

How Cone Health used predictive analytics to increase infusion center volumes by 8% while decreasing wait times by 25%

Like many health systems, Greensboro, N.C.-based Cone Health has faced nursing shortages in recent years. Unlike many systems, however, Cone Health's infusion centers have been able to maximize patient access, reduce costs through optimized scheduling and efficiently allocate staff.

During a recent Becker's Healthcare webinar sponsored by LeanTaaS, Melanie Rodgers, RN, assistant director of nursing for oncology services at Cone Health's Drawbridge and Wesley Long Cancer Centers, discussed how the organization's infusion centers used predictive analytics to increase daily completed volumes by 8%, decrease average infusion wait times by 25% and reduce appointments running past closing time by 78%. "With existing resources, we were able to see more patients and improve the patient experience while significantly decreasing overtime costs," Ms. Rodgers said.

Four key takeaways were:

"Most of our centers are 30 to 45 minutes away from one another, so patients can go to different sites for care depending on availability of appointments. Some of our locations also accommodate patients on Saturdays, so people don't have to take time off from work," Ms. Rodgers said.

Improved visibility into open appointments has also been critical for Cone Health's "Speed of the Patient" initiative, which is designed to move cancer patients from diagnosis to treatment more rapidly. "Instead of waiting a month for treatment, patients are starting treatment in two weeks," Ms. Rodgers said. "We can get people in quicker with iQueue."

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