Radiology groups around the country are grappling with issues from declining reimbursement to increasing regulatory compliance to exploding administrative burdens from insurance payers and governmental agencies. From the new Clinical Decision Support Mechanism (CDSM) process to radiology prior authorizations, practices are looking for ways to improve operational efficiency and gain some headway in the battle to improve bottom-line results.
Today, the most promising solutions involve technological advancements using AI-driven programs that streamline the more mundane and routine aspects of the billing process while increasing the revenue otherwise lost through denials and inefficiencies. From patient access through the revenue cycle management phase, AI-enhanced workflows supported by machine learning and predictive analytics are opening opportunities to capture revenue where it was previously lost.
Steps to Increase Operational Efficiency in Radiology
Incrementally, radiology groups stand to gain the most significant leaps forward by identifying AI solutions that can modularly expand and grow as technology dictates. Finding a solution that integrates with existing EHR/EMR systems through a dedicated HL7 connection that maintains HIPAA compliance creates the optimal situation allowing for further expansion as time or resources permit.
Let’s look at some of the solutions available today:
Everyone’s favorite villain—prior authorizations (PA)—is here to stay for the foreseeable future! But what if the bulk of your PAs could be processed in real-time, allowing patients to be scheduled at the time of service and procedures to move forward unimpeded? Would that improve the patient experience, lessen the administrative burden on business personnel, or reduce the providers’ stress and allow them to see more patients?
But how would it work? Once a patient makes initial contact to schedule their procedure and provides demographic and insurance information, or if the ordering information is received through the hospital or referring provider, the automated processing begins. A determination engine identifies patients requiring authorization, the system processes and submits an approval request electronically, requests are monitored and followed up as necessary. The finished PA is communicated back to the scheduling staff for appointing, all in real-time.
As radiology absorbs the new CDSM requirements handed down from CMS, some of the vendors that have been approved offer bi-directional communication and processing in real-time through a collaborative effort with ordering providers. This presents an opportunity to make the new process automated and manageable before 2021 requirements make compliance mandatory. Certificates can be obtained in real-time before a patient is seen, reducing the time required to backtrack once the claim is ready to be submitted.
Once a claim has been submitted, the hope is for clean execution and timely reimbursement. But when that doesn’t happen, radiology groups would gain increased efficiency by automating an AR optimization process. Rejected and denied claims would immediately enter an automated engine to determine the “next best action” using AI, predictive insights, and deterministic rules.
Through prioritizing and predicting successful recovery, denials would be corrected and resubmitted in real-time, thereby reducing days outstanding, as well as revenue abandonment from neglectful follow-up.
Relatively new, the process and impact of insurance discovery are just now beginning to influence stakeholders as consumerism continues to increase bad debt and collections problems. To outline, insurance discovery, through an AI-driven network of clearinghouses and insurance payer roles, determines possible insurance coverage that was previously unknown for patients that have bad debt or have fallen into the charitable care category.
Especially effective with Medicaid and commercial payers, once coverage has been identified, a new claim can be generated and submitted. Barring timely filing problems, these claims have been successful 67% of the time.
In summary, the Healthcare Financial Management Association (HFMA) determined that US Acute Care Hospitals alone wrote off $56.6 billion in bad debt between 2015 and 2018 and the Medical Group Management Association (MGMA) noted in a recent presentation that practices are losing between 3% and 5% due to inadequate RCM procedures. These issues are systemic but have automated solutions available for those investing in their futures.
Contact us to learn more about increasing your radiology practice’s operational efficiency and the ways it can improve overall financial performance.