Utilization management (UM) has become a huge administrative burden for both providers and payers due to multiple data sources and manual processes. By using artificial intelligence (AI) technology and real-time data, all parties can significantly reduce current utilization management review time.
In a Becker's webinar sponsored by XSOLIS, Matt Brink, director of payer business solutions for XSOLIS, and Jody Ranck, PhD, senior analyst for Chilmark Research, discussed the burdens caused by utilization management today, how AI technology can help relieve those burdens and the results of a recent study comparing traditional vs. AI-assisted processes.
Four key takeaways were:
- The traditional approach to UM causes administrative burden and friction. According to Mr. Brink, the utilization review process is highly manual and extremely time consuming. He cited a JAMA Network report that found about $1 trillion is spent on administrative healthcare annually in the United States.
"The traditional approach to utilization management involves using some evidence-based criteria to evaluate recommended care and the length of stay," Dr. Ranck said. "Payers and providers have access to multiple data sources, which can result in different outcomes for the review process. Then there's the appeals process. Payers and providers are not always on the same page."
- AI and machine learning can transform the utilization management process. "If we can automate and streamline this process while maintaining the evidence base with AI, we can decrease the amount of time it takes to conduct the review," Dr. Ranck said. "It would reduce friction between providers and payers, and could result in better outcomes for patients." Benefits that payers and providers are experiencing using AI and ML to tackle UM include:
- Reduced administrative burden and clinical overhead for case reviews
- Improved member/patient experience
- Increased clinical review efficiency through first-touch determinations
- Real-time updates
- Status alignment
- Case automation
- XSOLIS helps provide real-time data and AI tools to all parties. "Through our provider partnerships, we integrate with the EMR to consume data in near real time," Mr. Brink said. "Then, we're leveraging AI and machine learning to provide an objective view of the patients, which helps close some of the historic data gaps between payers and providers."
- A recent time study illustrates the results of using XSOLIS’ CORTEX compared with EMR-based processes. The study found that cases qualifying for precision utilization management were finalized 76 percent faster than EMR workflows, final determinations were reached 15 percent quicker through collaboration in CORTEX compared to provider EMRs and teams reached first-touch determinations 66 percent of the time, which is 36 percent better than through EMR access.
By utilizing AI and machine learning, providers and payers can create partnerships focused on reducing administrative inefficiencies and improving collaboration. XSOLIS also delivers capabilities such as predictive analytics around anticipated discharge dates as well as the ability to streamline the post-acute referral placement process. Using such tools, providers and payers can better understand data and then leverage all teams to work across a single platform.
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