A widely cited JAMA study recently published that the US Healthcare system spends anywhere between $765 and $935 billion on wasteful expenditures, which accounts for nearly 25% of the annual spending. This waste takes many forms such as administrative waste, which accounts for the largest share of the overall wasteful spending, amongst other domains such as lack of proper care coordination, high prices, as well as fraud, waste and abuse.
As it was widely being recognized that over utilization of healthcare resources was driving a large percentage of waste, estimated at ~$200billion, most health plans moved to value based payment models that paid physicians for achieving higher quality outcomes at lower costs. However, the shift to the value-
While most health plans have a payment integrity infrastructure in place to detect and dial back on the administrative waste, it is largely retrospective in nature that traditionally target pay-and-chase recovery solutions. A large part of payment inefficiencies are typically generated due to data silos coupled with complex healthcare policies resulting in erroneous payments that end are detected after the money has already exchanged hands.
Technologies such as AI are making drastic advancements in the early detection and cost avoidance. For example, AI is widely being leveraged in Claims Audit where smart audit algorithms are able to effectively identify incorrect claims (such as due to duplicates, amongst others) reducing administrative overhead, while correctly identifying claims that need expert manual review. With inbuilt feedback mechanisms in place, the system learns over time leading to higher accuracy rate and effective pattern recognition enabling smart root cause analysis to detect underlying problems.
Similarly, as our current pandemic situation further evolves the federal and state policies for Coronavirus testing and treatment, it is important to make sure that payers and providers have the most accurate and timely eligibility and enrollment information for seamless coordination of benefits (COB) implementation. Many payers are leveraging AI for driving operational and financial efficiencies through proactive identification of COB members and claims, reducing the need for post-payment audit and recovery.
The administrative burden of Payment Integrity also affects Providers and diverts the already stretched time and resources that should have been spent on patient care. One of the factors that leads to claim denials is the over utilization of healthcare services. AI, in combination with NLP and Robotic Process Automation can analyze patterns of over utilization much ahead of time such as during the prior-authorization process. Once these patterns are detected, a health plan can effectively utilize data and analytics as evidence to proactively modify provider behaviors and avoid provider abrasion.
However, even with the understanding that pre-payment cost avoidance models have inherent benefits, a drastic shift to these models will take time within the current technological and regulatory ecosystem. Payers must seek to deploy a combination of both pre-payment cost avoidance models alongside traditional post-payment solutions to realize maximum success through AI enablement.
While a significant portion of Payment Integrity relies on timely, more expansive use of existing information, Artificial Intelligence brings in the opportunity to handle leakages as patterns, streamline their identification, maximize recovery opportunities while optimizing traditional operations involved on this process. Ultimately, the traditional reactive approach to solving leakages will be transformed into a proactive identification, drastically reducing the need for recovery altogether. Targeted augmentation of the Payment Integrity operations holds the promise for significant financial upside for the payers adopting a differentiated mindset.