Machine Learning

Machine Learning In Action - 5 Revenue Cycle Examples

Post by
Bethany Grabher

In the revenue cycle, Machine Learning can drive a significant improvement on workflows, resource management, the cost to collect and most importantly, payment outcomes.

The beauty of Machine Learning is that it intelligently drives decision making. Unlike automation, which simply executes, Machine Learning dynamically understands the best course for action to achieve desired results. Integrating Machine Learning essentially adds a decision engine to the revenue cycle, enabling you to determine the next best action that should be taken with each claim, denial, patient account, payer contract or vendor.

Can't envision it? Here are five examples of what ML in the revenue cycle looks like, in action:

1. Denial Prevention - Machine Learning can proactively detect claims that are likely to be denied and send edits upstream to prevent future denials as well as provide actionable intelligence to functional areas like Registration, Coding and Payer Contracting.

Benefits: When implemented to prevent denials, machine learning works to improve first-pass yield, decrease denials and in turn, maximize revenue from payers.

2. Denial Prioritization - Using historical claims and remittance data, Machine Learning can rank-order current/future denials based on their overturn potential -- within your current system (i.e. Epic, Cerner). This helps your team stop chasing dollars and focus their work efforts on the denials that will actually be paid. This differs from the standard, rigid, rules-based approaches that focus on dollar amounts and age.

Benefits: Machine learning intelligently prioritizes denials -- working the right denials accelerates cash flow and increases bandwidth so employees can focus on resolving high-value denial issues.

3. Payer Performance - Machine learning can generate intelligence around claims, denials and overall payer patterns. This kind of unified intelligence enables providers to identify and mitigate underpayments, track denial trends, understand how plans relate to patient payments,  and benchmark payers and plans.

Benefits: Machine learning and payments intelligence equip providers to prevent and recoup underpayments and to better handle payer negotiations.

4. Patient Financial Engagement - Machine Learning can determine the likelihood and timing around a patient successfully resolving their account, as well as identify the most cost-effective option for follow-up efforts (i.e. internal team or external vendor). This intelligence enables your team to contact patients in the right way at the most optimal cadence as well as determine which patient accounts should be outsourced to a third party and which should be worked internally.

Benefits: Contacting patients with the right cadence improves engagement and increases payments. Understanding what to outsource reduces your overall cost to collect and allows you to make the best use of your internal resources.

5. Payment Plan Provisioning - Using historical payments data, Machine Learning can determine the best-fit payment plan for each patient. This includes the plan length, number of installments and any discounts. Identifying the best-fit payment plan can be done proactively, as early as scheduling, helping to get more patients on payment plans, earlier.

Benefits: Machine learning based segmentation and recommendations around payment plans lead to a higher payment plan adoption rate. Payment plans lead to more dollars collected, better patient engagement and increased patient satisfaction.

If you have wondered how machine learning could benefit your revenue cycle or you've explored implementing AI, we have a team that can answer any lingering questions or provide a full picture of the impact AI can have on the revenue cycle. Get in touch, we'll help pinpoint how AI and machine learning can address your revenue cycle challenges and goals.



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