AI, Health Care and Cost Containment

The United States spends an average of $3.8 trillion per year on health care. Some estimate that fraud, waste, and abuse cost the nation at least $114 billion annually — more than 3% of overall health care spending.

It's time for change.

Historically, health insurers, benefit plans, pharmacy benefits managers and other players focused on identifying problems after claims were paid.

Now, organizations are focusing more on pre-claim and pre-pay cost containment initiatives.

Part of that effort involves giving many different cost containment stakeholders, in many different departments, an integrated view of medical, facility, pharmacy and even dental claims.

So, what are the five best practices that health care organizations can use to create a holistic unified cost containment program?

Adopt self-learning artificial intelligence tools.
Create a unified view.
Enable better detection of exposures.
Create value-driven holistic reporting.
Empower all players in the system to work together.

Much of this is easier said than done.

Health care payers, in particular, still face the challenges of siloed data, organizational dynamics, rigorous compliance mandates, and outdated technology. Payers also face an increase in the number of compliance mandates and audits and dependence on resource-intensive processes.

As a result, payers have fragmented views of their health care payment spectrum, reporting gaps between different departments, provider abrasion, and duplicated efforts.

All of these problems lead to missed opportunities and waste.

Self-learning systems can help, by helping organizations monitor many different information streams at once, and by uncovering existing problems and detecting emerging problems before they hit the bottom line.

It's All in the AI

In a 2020 KPMG study on AI in the health care industry, 89% of respondents said AI is creating efficiencies.

For unified cost containment programs, AI can:

Process large volumes of disparate data, such as claims data, and derive the insights needed to create a unified view.
Identify behavior-based patterns and detect outliers and potential exposures as they emerge.
Quickly identify practices that intentionally or unintentionally waste money.
Explain new fraud schemes to the user, while gathering supporting evidence automatically.
Build connections across data to provide actionable insights.

The power of self-learning AI is that it delivers the rationale behind identified patterns, providing details and support for decisions that health care leaders make. The sophistication of AI-driven predictive analytics enables earlier and more accurate detection of outliers and financial exposures.

Holistic Reporting

One example of the new technology at work is Codoxo's Forensic AI platform.

The platform helps health care payers create a single view of the patient, by collecting data from many different sources, such as professional, facility, dental and pharmacy claims. The platform then offers behavior-based analysis of the data.

All teams focused on health care cost management — including the special investigations, payment integrity, network management, provider contracting and clinical teams — can use the platform.

AI-powered, unified cost containment programs can help health care organizations overcome the challenge of having to provide many different types of external reporting, including claim-level reporting; high-level summary reporting; and reporting designed specifically to meet the needs of providers, employers, payers, patients and other users.

AI-enabled holistic reporting can also improve information-sharing between an organization's own departments.

An AI-based system may even help payers reduce provider abrasion, by giving payers the information they need to communicate with payers about issues more quickly.

The Savings

Highmark's Financial Investigation and Provider Review department is an example of a well-known organization that has benefited from use of an AI-based cost containment effort.

The company added an AI-based system to its fraud, waste and abuse program.

The system helped Highmark assign providers risk scores, focus on the providers with the highest risk scores, and identify the use of unsupported documentation for procedure coding.

This generated more than $220 million in savings linked to fraud, waste and abuse in 2020. That includes about $130 million in savings related to employer-based health insurance; $50 million from the Blue Card program, which offers Highmark customers access to the national Blue Cross Blue Shield network; $22 million from Medicare Advantage plans; $9 million Affordable Care Act marketplace coverage; and $8 million from Federal Employee Program coverage.

As more health care organizations use AI to power unified cost containment programs, it's becoming evident that this advanced technology is ideal for finding savings and creating transparency down to the provider and claims level, and doing it faster than previously possible.

Rena Bielinski is a vice president at Codoxo. She has a doctor of pharmacy degree from the University of Illinois College of Pharmacy in Chicago. She holds the Accredited Healthcare Fraud Investigator professional designation.