How analytics helps healthcare providers overcome revenue cycle challenges
A growing number of healthcare providers are harnessing the power of analytics to improve patient outcomes and better track disease trends by tapping into electronic health records, clinical information systems and connected health devices.
Analytics also can be used to improve a provider’s financial health, yet a recent MIT/IBM survey shows that 34 percent of healthcare providers lack an understanding of how analytics can be used to boost their bottom line.
But there are forward-thinking healthcare organizations successfully applying analytics to improve the revenue cycle. Analytics are being used by some of these providers to identify bottlenecks, reduce claims errors, and lower bad debt.
Providers who use analytics in the revenue cycle gain a holistic view of data that are typically siloed in separate departments such as patient access, billing and collections. This allows them to see previously undetected flaws in their processes and identify ways to improve revenue cycle operations.
For example, a 600-bed healthcare system in the South had what it considered an unacceptably high level of denials. By collecting and analyzing data from various departments in the revenue cycle, the provider identified registration/eligibility issues as the root cause of its denials.
Through improvements to process flows in registration and obtaining eligibility data, the healthcare system lowered its denial rate from 11.6 percent to 1.2 percent over a four-month period. This lowered the total potential lost revenue from $2.54 million to only slightly more than $20,000.
Just as denials can adversely impact the revenue cycle, so too can unreleased claims, which inevitably lead to delays in receiving payment. A hospital in the Midwest with less than 200 beds used analytics to determine that its unreleased claims ratio was 27 percent higher than its peers and was resulting in $5.24 million in unreleased charges.
The hospital’s director of patient financial services launched a review of unreleased claims and instructed A/R to make getting claims out the door a top priority. Over a six-week period, the hospital reduced unreleased A/R days from 3.5 to 1.0 days and cut the amount of revenue stuck in limbo to $1.41 million.
Analytics also can enable healthcare providers to use predictive modeling to proactively identify individual claims that may need special focus based on time thresholds, dollar amounts and payer profiles. Anticipating potential claims problems can help providers avoid delays in payment, a major problem for many hospitals, clinics and private practices.
Since healthcare industry experts expect implementation of ICD-10 to trigger increased errors in the revenue cycle, providers would be well-advised to ensure that their revenue cycle is running as efficiently as possible as the October 1 deadline nears. Analytics can help healthcare organizations achieve that goal.
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