Patient Payments

How a 120-Day Machine Learning Experiment Led to a 6.5% Increase in Patient Collections

Post by
Justin Nicols

Terms like "AI" and "machine learning" seem to be incorporated in every healthcare IT company’s talking points lately. It sounds good, but what is the real impact on the healthcare revenue cycle? What do AI and machine learning look like in practice? Do they actually improve the patient experience or increase recoveries?

At Sift, we strive to answer these questions day-in and day-out when we’re talking to partners, potential clients, investors and industry leaders. We see firsthand the impact machine learning has on payment outcomes, work prioritization and patient experience. But, our talking only gets us so far. What really drives home the point and highlights the impact are results. 

AI - Results In Action

In late 2019, Sift Healthcare and State Collection joined forces to test AI in the revenue cycle. State Collection, which supports over 30 healthcare providers around the country sought out to determine if machine learning could actually be leveraged in healthcare payments or if these were simply empty "buzzwords" being used in the industry. 

Together, we conducted a rigorous 120-day live, scientific test (with a control group) for Wake Forest Baptist Health, to determine if machine learning impacted patient financial engagement outcomes.

In the test, State utilized Sift’s machine learning to segment patient accounts and optimize revenue cycle workflows. Simultaneously, State implemented Sift’s detailed analytics and insights to enhance the patient experience by using a personalized communication cadence and payment options. 

The results were impressive — a 6.5% increase in collections across all accounts (representing $3.9M). And, a unique twist, performance was maintained during the height of COVID-19 stay-at-home orders in early 2020. 

To learn more about State Collection, the results of our joint AI test and how Sift operationalizes AI in the revenue cycle, watch our joint webinar, or you can download and read the full case study.

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