4 Practical Uses for Artificial Intelligence in the Radiology Billing Lifecycle

Thin operating margins are the rule in healthcare today, and the future only promises to continue to tighten.  If you have a hospital affiliation, that margin can reduce even further.  While some solutions include expanding services, increasing patients, or negotiating better fee schedules with payers, radiology groups still have some untapped opportunities in the billing and payment lifecycle.

Using intelligent automation is a real game-changer leading to increased revenue and streamlined administrative costs.  Imagine the efficiencies gained by using a real-time, intelligent billing procedure that performs the multitude of processes automatically based on machine learning and predictive behaviors.

How Artificial Intelligence Improves the Billing and Payment Lifecycle

The bottom line for any radiology group when looking at patient access and revenue cycle management (RCM) will always be:

  1. Is it practical?
  2. Is it effective?
  3. Can it be easily implemented?

Here are four examples that would benefit your organization by bringing the strength of intelligent automation, each supported by trained specialists to process emergent and outlier issues.

1.    Prior Authorizations in Real-Time

Without a doubt, prior authorizations (PAs) are one of the most problematic functions performed in any practice or hospital business office.  Payers wholly dictate them as a way to manage care. Still, according to the widely-cited 2018 American Medical Association (AMA) survey, they delay access to necessary care, as reported by 91% of responding participant physicians.

For radiology, patients referred should have an accompanying PA, but far too often, they don’t, and it becomes the responsibility of the radiology group to obtain one.  With an artificial intelligence (AI) driven software that can seamlessly integrate into your existing EHR/EMR and billing system through an API or HL7 bi-directional based integration, a PA can be determined in real-time, with those required being generated and submitted to appropriate payers electronically.

With automatic follow-up and appeals capability, PAs would be returned upon completion 24/7, allowing for more efficient scheduling and significantly reduced claim denials.  

2.    CDSM Doesn’t Have to Create Problems with Medicare Patients

CDSM is now fully in place and, starting in January 2021, will impact reimbursement for all Medicare patients receiving advanced testing/imaging procedures.  In a strange twist, rendering providers will be financially penalized if the ordering providers haven’t consulted an Appropriate Use Criteria (AUC) tool and generated a certificate of compliance.

By utilizing a CDSM tool that can respond, both proactively and reactively, your radiology group can work in tandem with your referral provider pool to ensure that certificates are obtained.  Using AI-driven software, providers have access to a rich library of AUCs sourced from Qualified Provider Lead Entities (QPLEs) that are used to complete the process, which not only ensures reimbursement but goes miles in creating goodwill and loyalty with referring physicians.

3.    AR Optimization to Reduce Days Outstanding

By leveraging AI, automation, and certified billing specialists, your radiology group can capture more revenue from your aging AR.  An advanced AR Optimization solution would bring AI, smart prioritization, and machine learning capabilities to automate aging processes, including denials management.  After determining actionable insights, the next best activities could be activated to increase revenue and cash flow and reduce aged days outstanding.

4.    Insurance Discovery as a Solution to Bad Debt Write-Offs

The amount of reimbursement that is lost annually due to unknown insurance coverage and uncollectible amounts owed is staggering.  It’s been reported that hospitals alone have lost more than $620 billion due to uncompensated care since 2000.  But what if there was a way to capture a portion of that uncollectible revenue with no risk by simply analyzing patients’ information to determine if there was unknown insurance coverage available?

Through state-of-the-art technology using deep data mining and probabilistic analytics, AI-driven software can identify undisclosed insurance coverage, as well as discover problems in patient demographics, insurance profiles, and benefits determinations, on uncompensated accounts.  Especially productive with Medicaid and commercial carriers, once new or unknown coverage is determined, it can be actively pursued through the new payer.  This not only captures revenue that would have been otherwise written off but creates a tremendous opportunity to gain patient trust.

Data shows a significant number of practices still using manual systems for many of these activities.  Practical yet effective, AI-driven solutions could increase your radiology group’s bottom line by meeting or exceeding insurance companies’ expectations and decreasing rejected or denied claims.

Contact us to learn how AI-driven software can streamline your radiology group or practice.

About the Author

Infinx
Infinx Healthcare provides innovative and scalable payment lifecycle solutions for healthcare practices. Combining an intelligent, cloud-based platform driven by AI with our trained and certified coding and billing specialists, we help clients realize revenue, enabling them to shift focus from administrative details to billable patient care.

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