Healthcare providers are increasingly focused on protecting their revenue amid financial pressures from dwindling reimbursement and increasing competition. In this environment, data analytics — using technology to examine and make conclusions from data sets — can help organizations improve revenue cycle performance, payer contracts and, ultimately, the bottom line.
That was the consensus of panelists during the Becker’s Hospital Review 4th Annual Health IT + Revenue Cycle Conference.
Three takeaways from the discussion:
1. Pinpoint health outcomes. Payers are shifting to payment based on quality/value of care rather than the volume of care provided. Therefore, it is even more crucial for hospitals and physician practices to closely examine health outcomes such as mortality, readmission and patient experience. This is where data analytics can help, according to Natasha Kumar, director of revenue cycle management at Mayfield Brain & Spine in Cincinnati. She said data analytics can help hospitals get a closer look at their outcomes and use them to their advantage in payer negotiations. The data can also help identify areas where hospitals could gain more revenue.
2. Measure revenue cycle processes. Organizations can also use data analytics in the revenue cycle. For instance, they can forecast which patients are most likely to skip a scheduled medical appointment based on past behavior. "These small things can be predicted by data analytics. They can capture more accurate predictions of how the patient deals with an appointment and also which services [when skipped by a patient] are most likely to lead to lost revenue [for the hospital]," said Bo Shi, assistant professor of finance at Morehead (Ky.) State University. "Those kinds of things can be analyzed, can be improved by using data analytics." Matt Kilton, associate principal at ECG Management Consultants, said organizations can also use data analytics to identify where reimbursement delays are occurring in the revenue cycle and use that data to prevent delays in the future.
3. Keep limitations in mind. Limitations cited by panelists included the inability of certain data sets to show the true cost of services rendered, as well as the fact that data sets are not meaningful unless they are high quality. Mr. Kilton said when using data in talks with payers, providers should ensure they validate conclusions against the payer's data. "Often the payer's data does not match a provider's, and time and resources can be saved by ensuring both parties are working from consistent data sets," he said.
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