Precision utilization management: How artificial intelligence is reshaping UM

As healthcare continues to focus on value, efficiency, and effective management, timely and accessible data has become an increasingly powerful tool across the care continuum.

Technology enables the type of efficiencies within the care environment that allow providers to undertake their work unencumbered by activities that detract from their effectiveness or distract from their expertise. Simply put, technology makes it easier for health professionals to put their experience into action where it’s needed most.

As healthcare leaders look at use cases for technology – artificial intelligence and predictive analytics included – within the care continuum, they need look no further than operations and revenue-protecting activities like utilization review or denials management – for instance, hospitals and health systems nationwide are already embracing technologies that transform the way they approach case management, enabling staff to work more effectively and eliminate the operational hurdles that put revenue at risk.

Take the example of Carolinas HealthCare System. On a recent webinar, I spoke with Tonya Harrison, Director of Clinical Care Management at Carolinas HealthCare System, on how they have used technology to create a fundamental shift in the way their staff approaches the utilization management process. As part of this selective, data-driven approach – what we’re calling precision utilization management – Tonya shared some of the innovations they’ve adopted to ensure that the right patients are connected with the right staff at the right time.

Tonya revealed how Carolinas HealthCare has initiated an 'auto-review' process that automatically and selectively reviews an eligible subset of their Medicare patient population using a proprietary Care Level Score (CLS) as a threshold. Artificial intelligence and machine learning form the backbone of this Care Level Score, which incorporates past and present patient data to deliver a real-time assessment of medical necessity – including the likelihood of status changes. Integrating the CLS into this auto-review process provides value and efficiency, allowing Carolinas HealthCare to "repurpose, not reduce" FTE efforts and expertise. They also use the CLS to more effectively review cases, determine medical necessity, and predict likelihood of status across their overall patient population, lending standardization and consistency to their approach.

It’s important to note that this process supports, not supplants, clinical expertise. The auto-review function allows energy to be redirected towards cases that require a more nuanced, holistic approach – one that can only be provided by experienced nurses and physicians. At Carolinas HealthCare, Medicare beneficiaries account for approximately 28% of the inpatient population. Of this percentage, 32% were determined eligible for auto-review, based on criteria jointly established by Carolinas HealthCare that adheres to CMS’ Conditions of Participation. The auto-review scoring methodology achieves 99% accuracy at the current threshold, lending confidence to the approach and ensuring compliance for the system. The opportunity presented by this success is vast: currently over 58 million U.S. adults participate in Medicare, a constituency that continues to grow. As this type of technology continues to provide value and reduce cost, the potential impact at a national scale is considerable.

Solutions like this approach focus on using cognitive computing and machine learning to empower physicians and case managers to make data-driven status decisions at the point of care, safeguarding revenue and ensuring the highest quality of care for patients: yet the potential impact from this type of technology extends across the entire healthcare spectrum. As executives look to position their organizations for success in the future, aligning data and technology for favorable outcomes should be a foremost priority.

About Mason:

In 2013, amidst a healthcare industry characterized by huge revenue losses for hospitals nationwide due to unreimbursed or under-reimbursed services, XSOLIS began with the aim of providing a data-driven approach to addressing operational, compliance and regulatory issues. As an early employee, Mason seized an opportunity by leaving a large health system to help build XSOLIS from the ground up and further its mission of bridging silos in healthcare data and operations. Today, Mason serves as Vice President of Operation for XSOLIS, which provides cognitive computing-driven technology solutions to hospitals across the nation.

The views, opinions and positions expressed within these guest posts are those of the author alone and do not represent those of Becker's Hospital Review/Becker's Healthcare. The accuracy, completeness and validity of any statements made within this article are not guaranteed. We accept no liability for any errors, omissions or representations. The copyright of this content belongs to the author and any liability with regards to infringement of intellectual property rights remains with them.

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