Deductible Collection Case Study: To get a clear solution, ask a clear question

I often get calls and questions about how to get better information out of the billing system. Data can be mysterious, so I thought it might be helpful to share a data project we are currently working on to demonstrate how to solve a common issue: deductible collection.
 
Clarity on the question we want to answer with data is important. Without a clear question, data becomes overwhelming and counterproductive to our goals. In this case, my client is unambiguous on the question. “We are going to call our recurring rental patients this year and collect unmet deductibles. How do we get a list each Friday of patients that will have rental dates of service during the following business week?” she asked.
 
Good question.

Armed with it, we can think about the type and location of data we need to extract to get an answer. We want:

•  Active sales orders; those come from the sales order table.
  
• Payers that have deductibles that reset on January 1; we can filter those so we don’t waste time plowing through an enormous report and making calls that do not end in achieving our goal of collecting unmet deductibles.
• To exclude HCPCS that have zero charge amounts because they are capped or inventory-only transactions; we can filter those out, too, to keep our report lean.

After we have our initial data set, we haphazardly sample patients on the report to make sure they belong and the imported information is accurate. For example, we look in the billing software to be sure the sampled patient is actively billing and that they have a scheduled billing date next week. We want to make sure that the patients that are included in our query are the ones we want to call to collect unmet deductibles.
 
Once we have a working data set, we can begin to think about the end user. Since this is a call list, we query telephone numbers from the patient table and append them to our active orders so our customer service representatives (CSRs) will have easy access for dialing. We also consider the grouping and order. It will be easier for CSRs to call the Medicare patients, and then the Humana patients…so on and so forth.

We also think practically from the CSR’s perspective. What if there are more calls to make than time allows? We need to prioritize the list so the most valuable calls are made first. To do that, we sort our call list in descending order so the largest allowable amounts are at the top.

Our first iteration looks like this:

It is exciting to have something tangible to present, but this is just the first phase. Now our client will use this data and, with their feedback, we will make modifications to improve it. When those wrinkles are worked out, we will be able to automate the process to take even less time.
 
Cool, right? We’ll keep you posted on how it turns out.
 
If you are interested in learning more about how to use billing data to make and save money, please join us on January 26, 2017 for A Nerdtastic Guide to Making Money with Billing Data.