“Big data”: Can it really improve patient billing?

If you’ve been watching the healthcare landscape over the last few years, it feels like these words pop up constantly. Every company is proposing the “next-generation” solution — but what does this actually mean, and what type of ROI can your hospital or physician group actually expect to see?

At Cedar, we use data science and analytics to improve the patient billing process. What this means is that we use a unique approach that examines both internal and external signals to constantly optimize everything, from patient communications to payment options. We’ve found that looking at a sole source of data, such as credit scores, is a poor predictor of whether patients will pay their medical bills. Instead, that’s just one of the many data points we look at.

First, we look at internal signals that come from the data we pull from providers – these include things like a patient’s payment history or prior history of interactions with communications. We then look at external signals, or those from outside sources using the patient’s demographic data. This includes things such as the patient’s age, zip code, insurance plan, etc.

Putting all these signals together, we estimate a patient’s ability and willingness to pay. We then use this knowledge to optimize all interactions with the patient.

Our goal is to ensure that our use of data science is constantly informing every interaction with a patient. This means that we look at millions of data points to inform the communication method, messaging and payment options that an individual patient may see. We use machine learning algorithms to ensure that these are optimized over time to help ensure the best response from patients.

By looking at both internal and external signals, we can deliver a more streamlined billing and payments process for patients while helping providers improve collection metrics.