For all the potential AI holds on the clinical side of healthcare systems, the more immediately valuable applications may well be on the administrative side.
Writing recently at Forbes, Tom Davenport, Distinguished Professor of IT and Management of Babson College, described the efforts and successes of Texas-based Baylor Scott and White Health (BSWH), a 52-hospital academic medical system that is the largest not-for-profit provider in Texas, at, among other things, optimizing net revenue and improving their patients’ financial experience.
According to Sarah Knodel, BWSH’s Senior Vice President of Revenue Cycle, for example, the system implemented an automated, machine learning-based price tool that generates estimates of patients’ out-of-pocket costs before they receive care. “The system automatically retrieves real-time eligibility and benefit 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. (Moreover), the technology gathers and learns from insurance claims to improve the accuracy of estimates over time.”
Prior to implementation, notes Davenport, “it took a revenue cycle employee 5 to 7 minutes to produce one estimate, with limited accuracy. Now, however, 70% of the estimates are calculated without any human touch.”
BWSH is also tapping AI for “claim statusing” in the business office’s insurance collections department to automate the process of checking the status of outstanding insurance claims. Previously, human collectors logged significant hours tracking down payment statuses from insurers. Now, as a robotic process automation (RPA) 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.”
According to Davenport, “ 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, writes Davenport. “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.”
The overall goal, said Knodel, is to use these and other technologies “to develop more collaborative and innovative partnerships with payers” while also eliminating “the time-consuming and inefficient back-and-forth process of treatment authorizations and appeals in favor of something more automated and efficient.”
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