Whether you work in healthcare or are just a patient who has interacted with the healthcare system, it shouldn’t surprise you to read that healthcare costs have been going up and are expected to continue to do so into 2025 burdening all stakeholders. Despite the obvious need to cut wasted healthcare spending, research suggests that the industry as a whole isn’t very good at it, with administrative spending being one of the biggest culprits. One study found that nearly one-third of excess U.S. healthcare spending is on administrative expenses.
Today, the backbone of healthcare administration is claims processing. Despite the structure of a claim being standardized, the processing path that each claim follows is antiquated, with many inefficient, expensive, or error-prone steps being embedded within.
The question is why? First, the rules around medical coding, billing, and payment can be intricate and ambiguous, resulting in varied interpretations and decisions from one claim to another, undermining consistency in the reimbursement process. Second, claims adjudication processes rely on an arcane mix of inflexible old technology and a lot of duct tape resulting in an inability to keep pace with the evolving needs of modern healthcare reimbursement. Finally, the human-driven processes health plans have established to try to patch these holes – a collection of functions known as “payment integrity” – are themselves inconsistent, expensive and slow, creating a vicious circle of spiraling errors, delays and expense.
While there are many flavors of payment integrity, each of them boils down to four basic steps: codifying a particular type of payment rule as a policy, selecting claims to review based on whether they might violate the policy, reviewing each selected claim to perform a deeper analysis, and finally, to the extent a problem is identified, fixing the errors in the claim.
As you examine how payment integrity works today, you start to see its limitations. The process of codifying policies is often left incomplete, resulting in many nuances being lost and a blunt one-size-fits-all approach being applied instead. The selection process involves casting a wide net with imprecise queries, meaning health plans end up wasting time and resources chasing false positives, even when reviewers are in short supply. This high level of inaccuracy creates headaches for providers too, who are forced to spend time in disputes and appeals to get the right answer, adding to their well-documented struggle with burnout.
Even when the correct claims are reviewed, those reviews can take an extremely long time, especially for more complex audits, which both limit the volume that can be effectively tackled and make it very expensive to determine what the appropriate reimbursement is. The same issue is relevant for the fixing stage, which can involve re-processing claims, generating new letters to go out to patients and providers, and other labor-intensive tasks.
Why does payment integrity work this way? The systems and processes of payment integrity today emerged as a historical accident from the limitations of the software systems decades ago. As new regulations and payment rules emerged over tens of years, it became clear that the core software could not keep up. The natural solution was to apply a series of tactical, one-off patches to keep the system running, throwing in generous helpings of human expert interventions to keep the machinery from falling apart. All these years later, no one person can comprehend the complexity of the overall processing chain including thousands of moving parts, duplicated modules that all attempt to solve the same problem, and tons of stale rules only known through tribal knowledge.
Perhaps more importantly, the predominant business model for payment integrity, whereby vendors are paid a percentage of the savings they find, creates a strong disincentive for those vendors to help fix the root causes creating the errors in the first place. No errors, no vendor revenue. The net result is a world where the same kinds of errors need fixing day after day and year after year.
There must be a new way forward for payment integrity. For payments to be accurate, efficient, and impactful, health plans need systems that digest all the “rules of healthcare” and enforce them consistently at wire speed. Such a system would not stop merely at identifying and fixing errors but would be capable of accurately coding and pricing every claim as a full-fledged modern pricing engine. Additionally, there needs to be a new business model that directly aligns all industry participants towards the goal of paying claims correctly vs correcting errors on the back end.
With advancements in AI and automation, we’re moving toward that future, in which we deploy systems that can digest English-language policies, interpret medical records and contracts, apply fixes to claim errors, and re-price claims accurately, all while continuously learning and improving with feedback. When we do this, health plans will be able to super-power the transformation of the economics of payment integrity.
About Prasanna Ganesan
Prasanna Ganesan is the Founder and CEO of Machinify, which he started in 2016 with the goal of unlocking innovation for healthcare organizations through safe and transparent AI solutions. Machinify leads the way in providing health plans with AI software and services that address the root cause problems of administrative systems and processes. Prior to Machinify, he co-founded VUDU in 2005 (acquired by Walmart in 2010), where his pioneering work as CTO resulted in over 30 patents.