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Taking Hold of the AI Opportunity in Revenue Integrity

By Vasilios Nassiopoulos

Health care is realizing the promise of artificial intelligence (AI) across a broad and seemingly endless range of use cases. The opportunities for using advanced technologies to accelerate labor-intensive processes touch everything from research and operations to supply chain and point-of-care decision making.

Data-hungry revenue integrity and billing compliance functions are no exception. AI tools such as machine learning, natural language search, and anomaly detection are fueling new and expanded insights into revenue integrity, billing compliance, and quality assurance to advance process improvement initiatives. And not surprisingly, the value proposition of these tools is growing rapidly amid COVID-19 as health care organizations try to maximize reimbursements against notable challenges such as revenue shortfalls and rapidly changing regulations.

Getting out in front of the challenge of identifying new revenue opportunities and avoiding claim denials requires a proactive, data-driven approach that drives efficient recognition of issues, actionable insights, and better collaboration between billing, coding, and compliance professionals. Manual efforts will simply not suffice as health care organizations seek to accelerate productivity that mitigates risk, continuously monitors and audits processes, benchmarks against industry norms, and drives process improvement.

Understanding the Challenge and Opportunity
Prepandemic, many US hospitals were already struggling with operational losses, according to a study commissioned by the American Hospital Association. Notably, median margins were expected to drop from a narrow 3.5% to -7%, even with government support from the Coronavirus Aid, Relief, and Economic Security Act.

Adding to existing bottom-line struggles, the Centers for Medicare & Medicaid Services identified $29 billion in improper payments in 2019—a trend that will continue as regulatory scrutiny turns to COVID-19 incentive payments. Furthermore, the addition of nearly 750 new CPT and ICD-10 codes—on top of COVID-19 changes—has created a compliance nightmare that will only exacerbate a devastating financial impact if health care organizations are unable to change the current trajectory.

Notably, Hayes identified a 20% increase in denials totaling $2.5 billion related to COVID-19 coding and reimbursement challenges alone in the first half of 2020 among the company’s vast claims repository. That’s significant, and health care organizations need efficient methods of understanding their risk. It is in this space where AI can dramatically change current dynamics. When applied to revenue integrity processes, AI can search and analyze billions of rows of data in seconds, whereas manual efforts to do the same can take months, leaving health care organizations further exposed at a time when they simply cannot afford more bottom-line fallout.

To fully understand the opportunity, consider the impact of AI capabilities on the COVID-19 revenue integrity strategies of a pediatric academic health care organization located in the Southwest United States. The organization used AI to continuously monitor coding and billing processes to catch errors before claims were submitted, speeding reimbursements. This proactive approach uncovered $8 million in COVID-19– related charges that were at risk for possible coding and compliance issues.

The Power of AI-Backed Informed Audits
Most hospitals and health systems have annual work plans that call for periodic audits of billing and compliance issues. These strategies can inform forward-looking process improvement strategies, but they do little to get out in front of denials and compliance mishaps.

To speed identification of areas in the revenue cycle that need attention, health care organizations need to conduct “informed” audits—analyses derived from data-driven analysis of claims and remit data that consider an organization’s particular compliance and revenue risks. Notably, this approach is also used by federal regulatory programs such as Medicare Administrative Contractors and Recovery Audit Contractors.

Informed audits zero in on specific risk areas—such as COVID-19—guided by data within a health care organization’s own system. Driven by AI capabilities such as natural language search, the right audit can create visualizations of potential issues by rapidly scanning millions of rows of data and help revenue integrity teams elevate strategies by doing the following:

• tracking case mix index, elective surgery trends, and overall financial performance across payers;
• monitoring and predicting denial trends by functional areas such as patient access, coding, compliance, and patient financial service;
• identifying risk areas and implementing corrective action plans;
• detecting anomalies and outliers in organizational data that create barriers to revenue integrity;
• tracking hierarchical condition categories and COVID-19-related denials to maximize value-based reimbursements; and
• benchmarking and facilitating comparisons of performance against industry peers.

Health care organizations already operating within razor-thin margins must now address the complexities of COVID-19 billing and compliance. The ability to leverage AI within the revenue cycle provides a level of predictability and transparency that simply isn’t possible with manual processes.

Moving forward, health care organizations must be able monitor coding and claims prospectively, coupled with benchmarking performance, to help overcome existing and future shortfalls. Use of advanced AI technologies will promote optimal compliant reimbursement and help eliminate the drag on revenues created by denied and delayed claims.

— Vasilios Nassiopoulos is vice president of revenue transformation and integrity at Hayes.