By Juli Forde Smith, Director, ZOLL Data Systems

Advancements in data science can be felt throughout our everyday lives. We have the technical capacity to collect huge sums of information, and one area where this data is proving impactful is in revenue cycle management (RCM). The ability to source, harness, and leverage RCM data creates real opportunities to increase dollars collected by health care providers. It offers rich gains in optimized reimbursement, reduced administrative cost, and improved patient experience.

Innovations around patients’ unique financial characteristics are driving RCM performance improvements. A clean claim must reflect the best data associated with each patient to optimize reimbursement. Errors in any of the patient or insurance coverage data result in costly denials, increased days to collect, and higher labor costs.

Patient Demographic and Insurance Data Discovery Leads to Higher Reimbursement

While the value of complete, correct patient and insurance data is proven, often information technology (IT) teams focus too narrowly on the cost side of the AR optimization technology equation. This can be a result of organizational culture, how goals are set and measured, or how budgets are allocated. A broader view that recognizes the revenue increases and staff productivity resulting from revenue optimization technology investments is in the provider or health system’s best interest.

There are effective patient demographic data augmentation tools available to mine big data and return the most accurate, complete patient information available in mere seconds. This can eliminate errors and complete missing information before submitting a claim, providing significant financial gains and workflow efficiencies. Similar tools can also verify insurance information and even seek out additional, billable coverage that may have been previously undiscoverable.

Recent data from ZOLL Data Systems reveals that for every 1,000 claims identified as self-pay at the time of service, 344 have active commercial or governmental insurance coverage. Using an insurance discovery tool to locate coverage for these claims nets huge wins for providers, increasing average collection per visit, without increasing administrative burden.

Patient Financial Characteristics

Big data can also improve the likelihood of patients paying more of their out-of-pocket responsibility, which continues to rise. According to James Zadoorian, Ph.D., of ARxChange, technological innovations in self-pay analyzer tools that adjust to patient affordability characteristics can lead to as much as an 83% increase in collections from uninsured patients and a 22% increase in collections from insured patients.

Self-pay analyzer tools mine big data to examine a patient’s medical debt score, available credit, proximity to the federal poverty level, and likelihood to pay so providers can more easily determine the best strategy for collaborating with patients to collect payments. This data also helps fulfill regulatory requirements for compliantly discounting a patient’s portion of a bill.

The best and most innovative technology available to RCM professionals today leverages big data to reduce claim denials, capture more revenue, and reduce administrative cost. Understanding the trade-off between the cost of using data discovery tools and the associated returns help IT teams more clearly see the value of these investments.