By Trevor Biglin, Esq.
Healthcare providers generate enormous amounts of financial data with every claim submitted and every payment received. Historically, much of that information has been analyzed at the individual account level: Was the claim paid? Was it denied? Was the correct amount received? Should an appeal be submitted?
Artificial intelligence and advanced analytics are beginning to change that perspective. Rather than evaluating reimbursement one claim at a time, providers can increasingly analyze thousands of claims simultaneously to identify patterns that may reveal systemic reimbursement issues.
Consider a provider with a managed care agreement requiring reimbursement according to a defined contractual methodology. An individual underpayment may appear to be an isolated processing error. If analytics demonstrate that hundreds of similarly situated claims involving the same services have consistently been reimbursed below the applicable contractual rate, however, the issue takes on a different significance.
AI-assisted analytics can help providers compare expected reimbursement against actual payments across large claim populations. Claims can be grouped by payor, plan, procedure code, date of service, facility, reimbursement methodology or other relevant characteristics. Variances that may be difficult to recognize through ordinary account-by-account review can then become visible as recurring patterns.
The same concept can apply to out-of-network reimbursement. Although the appropriate reimbursement methodology depends upon the applicable contract, plan, law and circumstances, providers may be able to analyze historical payments for comparable services, payor-specific reimbursement patterns, geographic information, charges and other relevant data to evaluate how out-of-network claims are being reimbursed. Analytics can help identify whether certain services or categories of claims consistently produce reimbursement materially different from expected or historical benchmarks.
Identifying a pattern, however, is only the beginning.
Once analytics reveal a potential reimbursement issue, the underlying data must be validated. Providers should determine whether the claims being compared are truly similarly situated and account for variables such as network status, benefit plan, procedure, modifiers, dates of service, contractual amendments and reimbursement methodology. AI can accelerate this process, but meaningful human review remains necessary before conclusions are drawn from the data.
The next step is quantification. If a reimbursement discrepancy appears consistent, providers can identify the potentially affected claim population and calculate the difference between expected and actual reimbursement. What initially appeared to be a series of relatively insignificant payment variances may represent substantial aggregate reimbursement when examined across hundreds or thousands of claims.
This is where data analytics can become particularly valuable to legal strategy. Instead of approaching counsel with isolated examples of suspected underpayments, providers can present a defined issue supported by an identifiable claim population, reimbursement history and estimated financial impact. Counsel can then evaluate the data alongside managed care agreements, reimbursement provisions, applicable law, administrative requirements and dispute-resolution procedures.
Importantly, AI does not determine whether a payor has breached a contract or whether a particular reimbursement methodology violates applicable law. Nor does identifying a statistical pattern necessarily establish legal liability. Rather, these technologies can provide providers and their counsel with better information from which to investigate those questions.
As healthcare organizations continue adopting increasingly sophisticated analytical tools, one of their greatest opportunities may be learning to look beyond individual claims. The same data used to manage daily revenue cycle operations can help identify systemic reimbursement patterns, quantify their financial significance and provide the foundation for informed decisions about escalation and enforcement.
Abril Law works with healthcare providers to evaluate reimbursement disputes involving contractual and out-of-network claims. By combining claims-level analysis with the applicable contractual and legal framework, counsel can help providers determine when a reimbursement pattern warrants further investigation and, when appropriate, formal dispute resolution, arbitration or litigation.
Trevor Biglin, Associate, Abril Law, can be reached at tbiglin@abrilaw.com.
