How predictive analytics can boost the bottom line

Healthcare is about providing critical services to people, but as any hospital chief financial officer or revenue cycle manager knows, healthcare also is a bottom-line business. Providers that can’t pay the bills won’t be able to help any patients once the doors shut.

While healthcare profitability always has been a challenge – even prosperous hospitals typically run on margins of 3 percent or less – providers currently are contending with seismic shifts in healthcare reimbursement models as the industry transitions from fee-for-service to value-based payments that emphasize quality of care. Further, many consumers are seeing premiums and deductibles rise to the point where they simply can’t afford to seek treatment – or pay for the treatment they receive.

In a column at CIO.com, contributor Paul Bradley says the complexity of modern, data-driven health systems and the imbalanced progress toward value-based reimbursement presents unique, if not insurmountable, obstacles to provider balance sheets.

“American healthcare reimbursement is currently at an in-between stage. Some states are further along in the shift to value-based care/fee-for-value. Others are still predominately fee-for-service environments,” he writes. “That’s part of the challenge: Hospitals and health systems having to span two different worlds.”

Driving down length of stay and denials at Camden Clark Medical Center

Whichever world a healthcare provider is in, they need to get paid. Revenue, Bradley says, is “what powers the purchase of next-generation surgical equipment. It’s what supports those additional clinicians who staff the newly opened urgent-care clinic; it’s what enables the groundbreaking of a new asthma center in an area that is currently underserved.”

The need to continually invest in healthcare technology makes it imperative that providers running on tight margins are able to optimize their revenue cycles while controlling costs. Bradley argues that providers should leverage technology – specifically predictive analytics and automation – to achieve greater revenue cycle efficiency and better overall resource allocation.

“By leveraging a healthcare system’s own historical data, it’s possible to identify the normal revenue-cycle processes, model them, and then pinpoint the cases that exhibit anomalies,” he writes. “Technology can then make staff aware of the issues that truly require investigation, action and resolution.”

There are a number of specific ways that data analytics can be used to improve the revenue cycle. Jason Williams, vice president of business strategy and analytics at RelayHealth, explores some of these in this article.

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