Taking a comprehensive look at the causes of denied claims, you will find that more than half are the result of front-end patient processing, i.e., inaccurate demographics, missing prior authorizations, or insurance eligibility issues. While the remaining claims are due to post-visit issues such as coding errors, timely filing, and a host of miscellaneous issues that often feel punitive without cause.
At a recent RBMA symposium, a study (accepted industry-wide) quoted estimating the cost of reworking denied claims at about $25 per claim and that over 50% of denied claims are simply written off with no attempt to correct or resubmit. With the number of denials per provider at approximately 12-29%, this is detrimental to any organization’s sustainability and yet, has been an accepted mode of business for many years.
Proven Artificial Intelligence Solutions
Using artificial intelligence (AI) and machine learning throughout the healthcare payment lifecycle, it’s possible to improve the denials management process in radiology by virtually eliminating the manual, burdensome functions that cause issues once a claim has been filed. AI-enhanced automation allows an organization to capture actionable and meaningful data and then interact with a wide array of payers to accomplish painstaking tasks in real-time through bi-directional integration and communication.
Simply put, if your organization can automate front-end processes with a criteria-based algorithmic system submitting clean and accurate claims to payers, then you can greatly reduce the denials management process at the back end.
For example, let’s take a look at the prior authorization (PA) function. Long considered a necessary evil for providing patient care, radiology business operations spend an excessive amount of time and resources to process and follow up on the vast number of PAs that are required.
A Front-End Solution that Eliminates a Back-End Problem
According to the AMA, more than 91% of providers say that PAs have a negative effect on patient care by impacting clinical outcomes and causing delays in treatment if not outright abandonment of care.
By leveraging automation and AI-driven cloud-based technology to submit and update PA requests directly from a patient’s EHR/EMR, payer authorization could be obtained in real-time and patients scheduled efficiently. Creating a bi-directional integrated pathway allows automated status checks and updates to be communicated to staff through time-sensitive alerts and notifications.
With PAs monitored and submitted automatically, denials will be virtually eliminated, and revenue will be captured in a timely manner, either through insurance payers or patients (if that’s deemed appropriate). Equally important, the time previously spent on manually managing the PA process can now be shifted to higher-level functions and improving the patient experience.
For Those Denials that Inevitably Occur…
It’s the nature of healthcare – even with the best-laid plans (or automation), some denials will still inevitably occur. By establishing a strong radiology denials management system that has the following components isolated through AI-driven automation, you can ensure that claims are adjudicated or appealed:
- Recovery forecasted and prioritized for pending, in-process, and denied claims
- Predictive rules to determine “next best action” based on DOS checks, insurance benefits verification, CPT mismatch checks
- Machine intelligence-powered strategy that prioritizes follow-up and appeal activities designed to capture every possible dollar
- Ability to determine the root cause through analysis and recommend upstream improvements
Today’s AI-driven software encourages radiology organizations to improve their patients’ experience while providing staff with some relief from the burdensome patient access and RCM processes. Additionally, the cost savings in staff time and lost production (due to patient abandonment of treatment) offset the initial capital outlay for enhanced and upgraded technology.
Schedule a demo today to learn more about how AI can help improve denials management in radiology.