The healthcare industry is looking down the barrel of what the insurers say is a billion-dollar question: Are artificial intelligence-backed billing tools driving up healthcare costs?
Autonomous medical coding, AI-assisted documentation and ambient listening tools for notetaking all promise to reduce administrative burden in healthcare. But that has come at a price for Blue Cross Blue Shield Association of about $942 million over two years, according to research the insurer released last week.
BCBSA said the rise in spending is coming without a corresponding increase in treatment, which the insurer said it would expect if patients were truly sicker. Hospitals are increasingly billing inpatient stays as more complex than before, claiming more patients have a greater number of medical diagnoses that require higher reimbursement.
That means AI-backed billing technologies could be contributing to higher healthcare spending, the insurer said.
“If patients are truly sicker, we’d expect to see more treatment,” said Luke Chalker, BCBSA’s senior vice president of product and data science, in a statement.
“For example, we’re seeing significantly more anemia diagnoses at these hospitals without a corresponding increase in transfusions,” he said. “The disconnect between diagnoses and treatment suggests that AI is identifying more billable conditions, not sicker patients.”
However, the companies that create those billing technologies are pushing back on that accusation, insisting their AI is getting providers what they are owed under the existing rules dictating reimbursement for care between payers and providers.
“Making a claim like this is making it in the context of the current paradigm,” said Dr. Travis Bias, deputy chief medical officer of health information systems at Solventum, whose coding platform is used by more than 80% of U.S. hospitals. “We live in a fee-for-service system, we pay for volume, not for value. So yes, if you collect more codes and do more procedures, the payments will increase.”
The BCBSA isn’t the only payer facing higher costs. Analysts at PwC attribute a 9% increase in insurers’ medical costs next year to AI-backed billing tools.
Payers are worried that these tools are enabling upcoding, when payers or providers submit inflated diagnostic codes to garner more reimbursement.
Correction, not upcoding
Hamid Tabatabaie, president and CEO of Codametrix, a healthcare technology company that uses AI to automate coding for over 500 hospitals and health systems, doesn’t dispute BCBSA’s claims about rising healthcare costs. But he does take issue with the blame being assigned to AI.
Autonomous coding tools are increasing costs for payers, he told Healthcare Dive. But it’s not for nefarious reasons, although he couldn’t rule out very rare instances of healthcare fraud, waste or abuse. Rather, the tools are overwhelmingly capturing what providers couldn’t without technical assistance.
“When last year they submitted their claims, if they weren’t using a valid system, they were missing it,” Tabatabaie said. “And now this year, they have addressed it.”
And in the current fee-for-service environment, capturing a more complete picture of a clinical encounter will lead to higher costs, explained Bias.
Under a fee-for-service system, payers reimburse providers based on the volume of services they render and the complexity of care. This incentivizes providers to deliver more care to maximize revenue and payers to limit care to save costs.
But AI is now making it easier for providers to better capture what they are entitled to under existing rules, raising alarms for payers, according to Tabatabaie.
“Any optimization that a provider does that causes them to receive payment for otherwise what they weren’t receiving — they don’t like that,” Tabatabaie said. “Payers don’t want to pay any more than they absolutely have to.”
Tabatabaie argues that medical codes were never meant to be about billing in the first place. ”Codes are supposed to be codes of clinical concepts,” he said. “They got hijacked by payers because that was the most convenient way to do claims processing.”
Both executives say their tools are designed to capture a patient’s medical reality more completely, which has benefits beyond reimbursement. AI can help identify community health needs and allocate resources more appropriately, according to Bias.
To prevent inaccurate coding, both companies also emphasized the strict guardrails they’ve built into their systems, touting coding compliance guidelines to ensure accuracy.
A fix requires systemic change
The concerns expressed by payers may not last forever, though, Bias explained. New incentives and shifting behaviors for providers could address some of the cost challenges associated with using AI-backed billing platforms.
The healthcare industry, for instance, has been earnestly transitioning to value-based care since the Affordable Care Act was enacted in 2010. Value-based models reimburse providers based on patient outcomes rather than volume, incentivizing lower-cost, higher-quality care.
These payment models have yet to overtake fee-for-service, though. But it’s something payers should care about more, Tabatabaie said.
With greater adoption of these models, payers wouldn’t be reimbursing providers for every code they submit. A more complete picture of a patient’s encounter also supports value-based payment since the models emphasize medical necessity and appropriate utilization more than fee-for-service. This also shifts the incentives for providers, he said.
Bias also predicts providers and payers will change the way they document, code and pay for care as revenue cycle AI tools continue to develop. Much of this will boil down to AI’s potential to reduce administrative waste between payers and providers.
“It’s going to take a realignment of systemic incentives,” Bias acknowledged. “That’s a much bigger conversation … at a societal level.”
That conversation might have to take place sooner rather than later. Questions still remain about the disconnect identified in BCBSA’s analysis around rising diagnosis codes and flat treatment patterns.
Still, revenue cycle AI is inevitably here to stay despite these worries, as most providers use some form of AI for clinical documentation and coding.
This begs the question: Who pays the price? When payers shell out more for care, premiums increase and those costs cascade to employers and patients. Administrative burden may also escalate with more audits, denials and appeals. Bias noted that already, “large health systems are wasting billions of dollars simply rebutting, appealing denials.”
While vendors and payers debate who’s right about coding accuracy, the meter keeps running.