The reimbursement landscape for clinical labs has never been so challenging or so precarious. Patient and third-party payer responsibility continues to shift and there is no end in sight for continued changes and increases in government regulations and insurance requirements.
In a rapidly changing and consumer-centric healthcare payment lifecycle, diagnostic labs and more specialized labs focusing on genomic testing are at a unique disadvantage by the very nature of the service they provide. New advances in Artificial Intelligence (AI) and machine learning in the patient access and Revenue Cycle Management (RCM) arena are answering many of those dilemmas and putting labs back in the driver’s seat.
How AI Can Reduce Clinical Labs Pain Points
Bridging the lack of face-to-face contact is priority number one. Laboratory reimbursement is complicated by the fact that they receive and test specimens without ever encountering the patient. This leaves the clinical lab in a passive role when ascertaining patient demographic and insurance information—relying on the hospital or the ordering provider to initiate prior authorizations and adequately verify insurance coverage.
When receiving a bill, a patient may have never heard of the clinical testing facility. This can create confusion when they receive unexpected bills denied by their insurance, for reasons they don’t understand. It then falls to the overburdened lab personnel to follow up with the ordering provider and gently coax their participation to help resolve outstanding claims issues.
1. Prior Authorizations
The sheer volume of claims and low dollar collections required in the diagnostic lab industry can be overwhelming. With the cost to collect relative to revenue being so high, it is paramount that labs streamline the process of identifying prior authorization requirements for each patient quickly. Ideally, labs have the ordering provider’s attention during accessioning and that is the prime time to determine what is required and to elicit their help if necessary. If there is something questionable, then they must initiate resolution immediately with the ordering provider or hospital.
Using AI through a unified interface, patient information can be submitted directly to the insurance payer electronically, with all follow up being automatic. Any complex requests or exceptions could then be handled by trained specialists ensuring an overall increased completion rate and a significant reduction in claims denials.
2. Patient Responsibility and Propensity to Pay
With the increased popularity of high deductible health plans, patients have become the single fastest-growing group of payers. Unfortunately, individuals are drawn to these plans enticed by the reduced monthly premiums but are uninformed about the potential financial implications. When they are, in the end, responsible for large sums, patients are often ill-prepared or unwilling to pay their lab services debt.
To combat these issues, diagnostic labs can implement an AI-enhanced propensity to pay system that can analyze a patient’s ability to meet their obligations. By determining this in advance, labs could make educated and informed decisions and significantly impact their bad debt in a positive way.
3. Claim Denials
Diagnostic lab providers are required to participate in all or most of the insurance plans in their area if they want to compete in today’s healthcare market. With that comes very complex rules and regulations and the expanding opportunity for claims to be denied. The sheer volume of Accounts Receivables continues to grow for most labs as patient responsibility increases.
By bringing in an AI-driven, machine learning system to tackle claim denials, you can determine which unpaid claims to focus on and then prioritize for resubmission, ensuring resolution to maximize bottom-line results. Predicting collectible dollars for recovery, by payer and then tackling those claims electronically cuts down AR days significantly and then engages manual follow-up activities allowing staff to be more efficient.
Technological advances are now available allowing machine learning to lead the way on tackling healthcare’s most pressing reimbursement issues. AI can reduce clinical labs pain points to a minimum by simply incorporating electronic solutions and supporting that with human intelligence when exceptions exist.
Book time today to talk with our team about prior authorization and denial management solutions that will improve your pathology and lab reimbursement.