Once a patient has been seen, and a claim has been coded and billed, there are inevitably denials and rejections that prolong payment if not outright stop revenue capture. These rejected claims make up the A/R and must be worked individually to ascertain the problem and then collect the necessary information before resubmission.
Denials management is often cited as an ongoing problem in many practices. To optimize A/R, state-of-the-art AIdriven automation is available that can utilize proprietary recovery prediction algorithms to focus efforts on which denials are collectible so that energy is spent on the recovery of revenue and increase of early cash flow.
By reducing write-offs and identifying the next best activity through automated algorithms based on payer guidelines and procedures, a group can be assured the maximum revenue is collected. When evaluating claims management solutions, consider these AI-driven software features critical to achieving the long-term goal of permanently reducing revenue loss from denied claims:
- The ability to predict recovery, including forecasting the dollars potentially available and the timeline to achieve final collections. With machine learning algorithms, unpaid claims can be evaluated on several available parameters, such as aging, payer, and modality.
- Access to predictive and deterministic criteria prioritize follow-up strategy activities to maximize and focus human intelligence efforts where they can be most effective.
- Automated claim status checks matched with the most-likely cause, i.e., integrated insurance verification and eligibility data, CPT mismatch technology, and DOS and benefits check capabilities. Once the cause is identified, appropriate changes are made, and the claim is resubmitted.
- Auto-creation of required appeal letters, if necessary.
- Automated eFax capabilities, when required.
- The ability to perform a root cause analysis through operational analytics to find where mistakes originate upstream, including insurance verification, prior authorizations, or coding problems, so that processes can be reviewed and upgraded where necessary.
- Adaptability so that if additional areas are identified as automation candidates, integration is possible with ease.
A third-party billing system’s complexity requires diligent review and follow up when revenue is held up, and the bottom line is affected. With the technology available today that harnesses AI, machine learning capabilities, and predictive analysis, each patient encounter can be verified, submitted, and followed up in real-time. As reported in a recent Infinx Case Study, it’s conceivable to recognize a +15% improvement in 90+ days collections from A/R Optimization alone.