Home How RPA with Ai Help Improve The Healthcare Revenue Cycle at Baylor Scott & White Health

How RPA with Ai Help Improve The Healthcare Revenue Cycle at Baylor Scott & White Health

by sol-admin

As the largest not-for-profit healthcare system in Texas and one of the largest in the United States, Baylor Scott & White Health includes 52 hospitals, more than 900 patient care sites, more than 6,000 active physicians, more than 40,000 employees. Let’s learn how this large academic medical system lowered its administrative costs with AI and Automation.

Sarah Knodel, the Senior Vice President of Revenue Cycle there, oversees many administrative functions and has 2,500 people reporting to her. As the BSWHealth system has three major areas relative to the revenue cycle, namely to reduce their cost to collect, optimize net revenue and improve the patient financial experience, Knodel feels that AI and automation can help with all of them.

Knodel and BSWHealth have been working on one approach to these areas for eight years now. With a focus on improving pricing transparency for its patients, BSWH implemented an automated, machine learning-based price estimation tool from Waystar, a healthcare technology vendor. The tool generates estimates of patients’ out-of-pocket costs before they receive care.

Prior to implementing the tool, creating the estimates was a very manual process of combining disparate information from numerous systems at BSWHealth. It took a revenue cycle employee 5 to 7 minutes to produce one estimate, with limited accuracy.

Now, 70% of the estimates are calculated without any human touch. The system automatically retrieves real-time eligibility and benefits data from the patient’s insurer and combines this with charges and contracted rates to create an estimate of out-of-pocket costs unique to a specific patient. The technology gathers and learns from insurance claims to improve the accuracy of estimates over time.

No outside organizations evaluate hospitals on the patient financial experience, but BSWHealth has received positive feedback since implementing the tool. Payment options are discussed in advance of care, leading to 60-100% improvements in point-of-service collections across various clinics and hospital departments. Physicians are also happy with estimates being provided in advance because it leads to fewer cancellations of procedures on the day of service.

BSWHealth is also leveraging intelligent technology for “claim statusing” in the business office’s insurance collections department to automate the process of checking the status of outstanding insurance claims. In the past, a human collector would have to log into multiple payer websites or call them. Now, RPA and screen-scraping technology mimic the user signing into the payer website. As the RPA system gets the claim status from payers, the data is integrated into the workflow of the collector such that it never hits the collector’s work queue if it’s accepted and scheduled to be paid. Conversely, accounts that are denied and require immediate action are accelerated for review. The statusing RPA results in an exception-based workflow where only accounts truly requiring human intervention are brought forward for review by a collector.

Sarah Knodel said that her organization is undertaking many projects of this type and using machine learning or RPA in almost all revenue cycle departments. In areas like utilization review, new technology reads medical record documentation in real-time and predicts whether a patient should be in inpatient or observation status, ensuring compliance with regulatory and payer requirements. As a result of that effort, BSWHealth has reduced FTEs in the utilization review department by over 20% while reducing payer denials by the same percentage.

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