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The VP of Patient Financial Services for an Eastern university hospital pointed to a troubling trend in his reimbursements. Delayed billing reimbursement losses in the last year, including timely filing limit (TFL) expirations, exceeded $5M across his organization.
While only a small portion of the hospital's total revenue, it's still a large number. With diligence, it should be dramatically improving. We wondered together why it couldn't approach zero.
Performance wasn't improving, and data demonstrated that Vee Technologies hadn't focused enough energy on accounts as they approach the TFL. A steady stream of accounts continually expired on our watch - running $2.1M/yr.
We strive to commit accuracy, competency and teamwork to Kurt's team while ensuring that we meet all of our SLAs. Due to this, we needed to dig deeper into the problem.
The hospital’s practice management system, from a top-tier supplier, employs complex account processing rules. Managers and consultants continually tweak account logic to employ new insights. Working together, we found two problems in the account flow.
In the first case, as we followed failed account paths backwards, many were getting stalled in a holding queue. The queue needed business rules to trigger actions before missing information prevented the claim from reaching the carrier by the timely filing deadline. By managing down the Pareto of stalled account causes with tailored business logic, we solved for the first layer of delays.
Focused on improvement, we started updating our agent training documentation to cover the rarest situations.
But as we assessed the gains and remaining gaps, TFL losses were still way too high. Little improvement followed from modeling business logic fixes in the PMS, deeper training on exceptions, more sophisticated reporting, or SME review gates.
When stepping back to look at the case field however, the accounts did share one characteristic. The progress of each account looked like a frog being boiled! Nobody realized the account was in trouble until it was too late to do something about it. We needed a rethink.
At the root cause, the account priority algorithm was too simple to recognize when an account needed a longer path for resolution. It also didn’t forecast the opposite case – accounts likely to be paid on their own without further attention. We had to spot and focus resources on the long-path accounts earlier in the AR lifecycle – the frogs – before they boiled over.
Beyond critical factors like bill size and time-to-TFL, every account was prioritized roughly the same. Using the queue rules embedded in the PMS system, our agents normally processed accounts in the designated order.
The data showed that when we processed accounts that didn’t need attention with the same priority as accounts that needed more focus, we would always see a statistical expiration of delayed accounts. Some would pass the TFL boundary, even with critical attention.
We needed to focus on the right accounts by applying a higher priority to the troubled accounts and a lower priority for accounts on a track to resolve themselves.
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