- Business Process Transformation
Analytics For Better Insights.
One of the nation’s largest healthcare networks and one of New York’s largest private employers.
In the midst of enormous growth driven primarily through acquisitions and expansions, our client was struggling to keep track of all of their patient accounts and because of this, was suffering from a higher-than-normal amount of bad debt write-offs with their Medicaid patients. The client knew they needed better insights from all of their new patient account data and needed to reengineer their collections process to lower the volume of write-offs. The client established aggressive goals to reduce their bad-debt write-offs related to unpaid patient bills to a goal of 3.5% from their current level of 4.4%.
Embracing digital transformation, Sutherland took a view of how to deploy digital technologies throughout the revenue cycle management (RCM) process and how to best apply analytics to improve the client’s collection efforts. Sutherland began by moving all the patient data into smartHealthConnectTM, our secure data hub. We combined years of the client’s historical accounts receivables (A/R) records with years of Sutherland’s operational data from working with the client. This data was then integrated with Sutherland’s suite of analytics tools, giving our data scientists better insights into the client’s accounting data, which helped kick off a complete re-evaluation of the client’s end-to-end RCM operations.
By leveraging the data hub, Sutherland had better insight into the client’s accounting data and began to re-evaluate the entire RCM process.
With the insights gained through smartHealthConnectTM and our analytics tools, we created a brand new digital reporting system that started with eight interactive dashboards and included 25+ real-time metrics for the client. The reporting system also provided claims denial reports, which showed the client their latest denials activity and the distribution of denials across the client’s different specialties. This allowed the client to track metrics in real-time and quickly act on the trends in their operations.
Sutherland improved the reporting and analysis of bad debt write-offs and improved the efficiency of collection efforts through analytics and machine learning algorithms. With these efforts in place, Sutherland proactively identified $2.3 million worth of invoices that had a high likelihood of being written off as bad debt. To date, the client’s bad debt percentage is at its lowest levels ever. Bad debt write-offs have reduced from 4.4% to 2.75%, well ahead of our client’s original goal, compared to the same period in 2016.