Magazine
Intelligence Unit Special Reports Special Events Subscribe Sponsored Departments Follow Us

Twitter Facebook LinkedIn RSS

How One Healthcare System Recovers Underpaid Claims

Karen Minich-Pourshadi, for HealthLeaders Media, March 16, 2011
Are you a health leader?
Qualify for a free subscription to HealthLeaders magazine.

For nearly two decades hospitals have relied on contract management systems, believing that these systems were accurately flagging payment variances. But as payer contracts have become increasingly complex, many systems have become incapable of accommodating routine updates and retroactive billing, which is resulting in millions of dollars being left behind in underpaid claims.

Predictive analytics, or data mining as it?s sometimes referred to, has the potential to uncover millions of lost dollars in the revenue cycle?from business process enhancements to underpaid claims.

Presbyterian Healthcare Services, a system of eight hospitals based in Albuquerque, NM, believed its technology was getting the job done, explains David Hennigan, the system?s vice president of revenue cycle. That is, until Presbyterian added data mining technology and uncovered nearly $32 million in missed charges and underpayments.

Like so many in healthcare, PHS was feeling the financial pressure to tighten its belt. Hennigan says the organization was interested in automating its credit balance processing and called on Apollo Data Technologies in Chicago, which suggested predicative modeling technology. Hennigan didn?t anticipate uncovering any data that showed the system was losing huge sums; rather, Presbyterian was looking to find inefficiencies in the revenue cycle and added technology to help contain costs, generate revenue, and increase staff productivity.

After a brief implementation process, PHS ran a data assessment of payer contracts and analysis of integrated data sources (categorized patient, financial, and clinical data). Within hours, Presbyterian learned that its contract management system had failed 40% to 50% of the time to identify accounts with underpayment variances and the result was a loss of over $20 million in revenue over a two-year period.

1 | 2 | 3

Comments are moderated. Please be patient.