Micro-segmentation of patient accounts and machine learning equip healthcare providers to shift to an ROI-focused approach to patient collections -- while maintaining empathy improving patient engagement.
In late 2019, Sift Healthcare and State Collection joined forces to test AI in the revenue cycle. 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. The results were impressive — a 6.5% increase in patient collections.
While triage around COVID-19 continues, patient bills are going to pile up, cash on hand is going to dwindle and a more significant number of patients will struggle to pay their medical bills. Now is the time to be strategic and truly commit to being a flexible payment partner for patients. The starting point? Data analytics. Which easily dovetails into machine learning, both providing a powerful advantage for patient collections.
Providers of all sizes, as well as RCMs, will be challenged in 2020 to drive better results in patient collections. Here are six ways to maximize patient payments. They might require some data science, but Sift makes that accessible.
For the patient revenue cycle, when, how often, and the method of outreach have a direct impact on dollars collected. But nobody is talking about contact cadence....
When healthcare providers leverage their patient payments data to drive their collections strategies, they create new and powerful opportunities to increase the revenue they collect, building patient relationships and using payments data to inform business operations. Here are four new and intelligent ways that healthcare providers are using their patient payments data to be more strategic and to drive better payment outcomes. Here are four innovative ways that healthcare providers are leveraging their patient payments data
Consumer debt on credit reports is not an indicator of a patient's (or rather, consumer’s) ability or willingness to pay their healthcare bills. Most propensity to pay models rely on credit scores. Healthcare providers need to know how their payments perform — taking into account the uniqueness of their facilities, providers, specialties and regions.