Why Credit Balance Analytics Are More Important Than Ever

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Crowe published a study last month quantifying the details of “the patient as the payer” and as you might imagine, none of the news was good. The study looked at INSURED patients and measured the outstanding account balances along with the likelihood of collecting those dollars. Health insurers have been cost shifting to patients for years and the creation of high deductible health plans, meant to make insurance more affordable, were the genesis of this problem.

Again, keep in mind that this data reflects INSURED patients. The numbers are jaw dropping. The number of patient accounts with a balance greater than $7,500 has tripled and those with a balance greater than $15,000 has quadrupled. To make matters worse, for any account with a balance greater than $7,500, the likelihood of collecting is close to zero percent.

The way that EMR’s are designed and configured is a major contributor to the problem. Typically, HB data is stored separately from PB data, creating a data divide that is a money losing proposition. Credit balance analytics are the solution to bridge that gap.

A large east coast academic medical center set out to tackle this problem late last year and the results have revolutionized their credit balance processes. Given the ability to easily track MRNs across the gap to see if there is credit on one side of the house and a debit on the other side of the house was a game changer. They were able to quickly identify 7,257 unique accounts, across 4,351 unique medical records that had both outstanding credits and debits that were cross applied for resolution either by transferring the balance or (automatically) issuing refunds. They saved on operational costs, cut back on vendor commissions, and even reduced bad debt. Embracing advanced analytics and automation of the credit balance resolution process freed up staff to focus on other credit balance work resulting in a 25% reduction of outstanding credit balances within the first 30 days. Overall, they reduced the cost per record for credit balance resolution by 90%, reduced average time to resolve to just 33 days, and in the first 90 days reduced the overall inventory balance by 42%.

Matching undistributed cash against debits shouldn’t be this hard. Using advanced analytics and automation helps revenue cycle teams do more with less at a reduced cost, while collecting every dollar possible.

Interested in learning more? The team at VisiQuate is focusing on how we can help hospitals optimize their revenue cycle management. Visit our Revenue Cycle Playbook for step-by-step plays to help you stay on top of the ever-changing landscape of healthcare revenue cycle, or contact us to schedule a demo.

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