The experience of medical billing isn’t pleasurable for any party. Anyone who’s been to a hospital or doctor’s office knows the drill: Healthcare providers and insurance providers try to both offer and gather information at the point of care, and when that information isn’t perfect (which it never is), fixing it gets passed down the line, eventually landing in an agent’s lap.
Boom! Time to get dialing. Their back and forth phone calls with insurance groups and the patient take time. They take training. And errors or poor performance can — and do — culminate in a collections nightmare: money tied up in collections that never had to be there in the first place.
But RCM doesn’t come with any quick fixes. Sure, you can try to add in as many self-pay options as self-pay platforms can offer. You can get closer and closer to fully accurate coding from the get-go. But the truth is, RCM functions and the agents that provide them are missing access to one of the most important data types: agent conversations with the consumer. And in the rare case they have it? They’re not enabled to use it.
Here’s how to start getting (and using!) more accurate conversation data and change RCM for good.
Yes, really. QA is table stakes in loan servicing and collections, and should be table stakes in healthcare billings cycles, too. Why? You don’t know what you don’t know: Sampling 2-5% of calls simply won’t cut it, and sampling based on QA tags from an internal resource can provide similar bias. Instead, you need a way to:
a) process a high volume of data accurately and
b) assess that data for certain key information about the conversation, such as compliance-related triggers and alerts.
What could you do if you were able to see huge gains in productivity? You could collect more with fewer agents, redistributing your best agents to new roles that require strategic thinking and analysis. But reaching your goals depends on the goals you set just as much as the actions you take to get there — and when you close that gap between goal and action, actions become the clear path to the goal.
So, for example, rather than evaluating your agents on wrap-time, what about note-taking time? The action is the way to the result: Decrease note-taking time and you’ll see a clear decrease in wrap-time, but without any of the ambiguity agents might feel about more “complete” goals.
This gap-closing KPI practice can help you understand productivity with more clarity, and bring you closer to accurate analysis of your workflows and processes — which brings you closer to those major agent performance gains.
When conversation data is in your hands, you can start to understand how people respond to tone and how what your agents actually say affects your outcomes. You can begin to make correlations between your payments or the progress of the calls and their capabilities. And one such correlation has become clear to Prodigal after 100 million calls analyzed: Your agents need to express empathy to find success in receivables.
So, to start your path to empathic expression today, consider the impact of empathy in every conversation you analyze. Start enumerating ways agents might express empathy. Begin to consider how you can cut down on lack of focus during calls, so agents can give their all to the conversation, as stronger focus on the conversation at hand is critical in an emotionally-fraught environment such as a medical billing phone call.
Now that we’ve illuminated some of the problems you might face in healthcare receivables management and RCM as a whole, it’s time to talk about some of those problems and their solutions more in-depth. Read our upcoming white paper to understand how you can achieve major operational improvements and increase profitability through using conversational data. Meantime, learn more about Prodigal below.
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