April Wilson, RevSpring

April Wilson, RevSpring

There is a lot of talk right now about how hospitals could and should be using “big data” to improve both clinical outcomes and the overall patient experience. However, very little of this conversation really breaks down what “big data” really is or how to strategically apply it to Revenue Cycle processes.

The reality is that the whole “big data” and “data science” hype boils down to nothing more than three basic concepts and an organizational agreement on how to proceed. Those three concepts are:

  1. Data
  2. Insight
  3. Decisions


Most HIS systems capture more meaningful data but in many cases only limited data elements are reported or utilized to help improve Revenue Cycle performance.  Right now, your systems and reports might only be designed to capture and pass along a few key data elements that are needed in a traditional patient billing process. This data may include patient and guarantor contact information, details about the services performed, insurance information, any related adjustments, and some key dates like service date, discharge date, and payment due date.. Once a patient makes a payment, you may also capture how that payment was made i.e., (lockbox, online, phone, text) and payment details (ACH vs. Credit Card, amount paid, approval or decline rates, etc.).

Some of this data might be aggregated into reports, in Excel or other reporting formats, helping measure cash flow and average A/R days to reconcile financial forecast and measure performance against budgets. What makes “big data” big is how much data you want to or can capture about any patient. In the other industries that could mean capturing social media data, like what they say about you, how often they say it, what website they say it on, etc. It may also include email interactions like opens, clicks, and unsubscribes. For hospitals, you might want to create new data elements like appointment adherence, prescriptions filled, treatment taken, rate of recidivism, response to treatment, and so on. For patient accounting, you might want to capture data like channel response (do they pay by phone, in the mail, online?) or communication sent (do they do better with detailed or summary statement, color or black-and-white, graphics or no graphics).  You will need insight to guide what other data elements to capture to grow your “big data.”


However, until you really use data to measure what’s working and not working in your current environment; data is just data. It lies there, dormant. The reality is that you probably capture a lot of data already that, if properly mined, can give you a lot of insight into your communication effectiveness.  For example, many hospitals have the data needed to understand how many guarantors responded to different statements that have been sent.  Data also may exist to understand the average balance paid by dunning level related to an outbound communication type (paper statement, IVR, email, etc.). You may also be able to calculate the average days from discharge to payment, average balance paid, and which guarantors would be better off with payment plans or prompt-pay discounts based on past behavior.

This is where an analytics partner or an internal analyst can provide great value to your organization.  An analyst will have the training and resources to provide insights regarding data and how it pertains to patient behavior and revenue cycle performance.  – You may have people with these skills in your marketing or digital departments that can provide this valuable insight. Although IT and Finance resources may have the ability to provide operational metrics they typically do not have the skills and expertise to assemble performance metrics to drive business decisions.


Once an analyst mines your data, she can provide more insight into what’s happening and where the best areas for improvement lie. For example, you may learn in this process that the slowest performing statement (in terms of payment) is the first statement with almost twice the A/R days of your second statement. This is where you, as the Revenue Cycle Manager, may decide to test new ways to make the first statement generate more payments. Is there a better way to display information about their care so that patients understand what they’re paying for? Is there a better way to display the billing elements on the page so that patients easily understand the amount due and what insurance has already covered?

In this example, a “big data” strategy would be to capture more data about the statement itself. You might want to know – if you are testing different designs – which design generates the highest payment rate?  Or, which design generates the fastest payment rate? Or, which statement generates payment amounts that are 100% balance paid? Not only are you capturing letter types, but you could also be capturing small variations. For example, what if the balance due is in blue instead of red? Does that impact response?

These are the kinds of analyses, strategies and decisions that non-healthcare companies have made for years.

Starting an analytics process and incorporating “big data” can be overwhelming.  An important thing to remember is to start small and grow only what you need to improve performance -stay focused on one outcome at a time.

For RCM professionals ready to start putting their data to work for them, RevSpring is hosting a free 45-minute webinar on Thursday, April 3. Analytics 101: The Basics will walk attendees through using analytics to optimize their current workflows and RCM processes.

Click here for more information and to register.


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