Improve cash flow and cost of reworking denials with the efficientC claim scrubber technology

Sponsored by efficientC.

How do you help healthcare organizations navigate the biggest challenges in healthcare?

The efficientC software platform was built specifically to prevent denials. In 2017, HFMA published a report that noted of the $3 trillion in healthcare claims, $262 billion ended up as denied dollars. A follow-up report, five years later, now shows that denial rates have climbed by 20%. The cost of denials is getting worse not better.

If a claim denies the first time, there is less than a 40% chance it will get paid on the next submission and a 60% chance it will deny again. This adds costs to your claims management processes and increases the risk of write-offs.

EfficientC’s claim scrubber technology and predictive analytics were built to stop claims before they deny. Our focus is getting as many claims paid on first submission as possible (First Pass Yield Rate). EfficientC customers, on average, get 95% of their claims paid on first submission which has a huge impact on cash flow and the cost of reworking denials. To accomplish all this, we work with our customers to apply new edits and change routines regularly at no additional charge.

What advice would you offer to healthcare leaders when choosing among vendors?

What do their customer say about them? The best way to confirm what a vendor has told you during the selection process is to talk to their existing customers. Ask these customers how they were treated after they signed the contract and about the ongoing support they received as well.

Is the vendor considered an expert in their space? Do your research and understand whether they are thought leaders in their product or service. Thought leaders will be more innovative, more likely to adapt to changing circumstances and staying current.

How will you measure success? Make sure that you know what the criterion for success is and how it will be measured. At efficientC, we set a First Pass Yield Rate goal of 95%. With efficientC’s predictive analytics, it’s easy to monitor results and adjust when needed. Our analytics were developed to provide a high-level view, with the ability to drill down for root cause analysis.

What is some advice you can give providers for a successful implementation of a new product or service?

Change can often be hard and disruptive. But it can be a great thing. When you choose a new product or service, your goal is to move your organization forward. In today’s challenging reimbursement environment, you can’t afford to not invest in the best tools available.

When selecting a vendor, make sure you understand the impact of an implementation to your team and your organization. A planned, structured implementation will no doubt be a benefit to both the vendor and their customer. Vendors should be able to provide a documented timeline with resources to assist in planning.

At efficientC, our proven implementation plan is designed to reduce the impact on our customers by putting the heavy lifting on the efficientC team. We know that we can accomplish everything within a time frame of eight to 12 weeks. Our implementation does not end at go-live. Our team stays with the customer until they are comfortable and effective operators of the system. 

Quick Facts

• 100% of peer reviewers would recommend efficientC to their colleagues

About efficientC

efficientC is a comprehensive decision support and claims management technology platform. It combines powerful denial analytics, claim scrubber and clearinghouse technologies, to deliver the finest denial prevention tool on the market today. efficientC’s Insights, is a predictive analytics solution that leverages machine learning to provide actionable intelligence to help keep denials at bay. Built on the backbone of its proprietary and powerful claim scrubber and stellar support team, the efficientC platform delivers the best possible results to hospital revenue cycle teams.

To learn more about HFMA’s Peer Review program, visit hfma.org/peerreview