- Key insight: Learn how AI uncovers benefits opacity, enabling transparent pricing and smarter plan stewardship.
- What's at stake: Employers and brokers risk higher costs and reputational exposure without proactive AI-driven claims oversight.
- Expert quote: AI should not just detect errors but drive recovery actions, says Reclaim Health CEO Youssef.
Source: Bullets generated by AI with editorial review
Advances in artificial intelligence are pulling back the curtain on benefits opacity, as seen in some of the latest point solutions being recommended in an increasingly crowded market. By
One such example is Collective Health, a third-party administrator whose concierge benefits navigation system helps health plan members
AI is meant to support the quest to provide them with convenient access to whatever it is that they need, make sure they're not being denied coverage unfairly or overcharged, and assure them that their money is being spent wisely, observes Ali Diab, the company's co-founder and CEO.
"There's a lot of complexity to the way that we administer these plans, and they usually have idiosyncrasies to them," he says. "We're deploying AI to observe and understand what's happening, and then allow us to ask the system questions about claims that weren't processed or processed in the way that we expected them to be and ask why that's the case, and then the AI can actually go and orchestrate an answer."
Read more:
As AI listens in to Collective Health's call-center audio, he says it retrieves, parses and then presents the information to member advocates who can respond more effectively without having to take copious notes. It also allows them to be more engaged and empathetic.
In addition, AI can pursue action that member advocates would not even think to do such as issuing new cards for a member calling in or run investigating something that the member asked about.
Dispensing business intelligence
With Angle Health, whose system produces underwritten quotes on custom plan designs in minutes vs. weeks, brokers and advisers are able to actually offer business intelligence on what's driving costs for their clients.
When uploading a member-level census, Angle Health co-founder and CEO Ty Wang explains that they're able to produce instant or near real-time underwritten quotes. As a result, he says the speed with which quotes can be delivered expediently will allow them to administer a larger book of business.
In partnering with benefit brokers and insurance agents who serve small and midsize businesses (SMBs), he says the aim is to help shop for and obtain quotes on health benefits with a level of transparency they historically haven't experienced.
Read more:
He sees considerable value in putting healthcare premiums and rates into context for SMBs when they're coming up on renewal and then allowing them to customize plan designs much the same way that large self-funded employers do with far greater resources.
"We have our own proprietary AI models that we've now trained on these data sources with millions of de-identified patient records for medical claims and pharmacy data," he explains.
Another example involves Reclaim Health, whose AI-powered, healthcare financial advocacy platform generates savings from uncovered billing errors on group medical, pharmacy and voluntary benefit claims.
Helping make health insurance benefits more affordable involves both retrospective and prospective claims review, notes Nataly Youssef, Ph.D., CEO and founder of Reclaim Health. "You start with what money are you already owed, and that becomes the overpayments made on your bills," she says.
The next step is helping employees understand how their health plan is performing and trust that it's working as designed, then evaluate other options in the context of their own claims data. "That's contextually a 360-degree view of all the care needs that someone is seeking," she says, noting that it allows employers to model different benefit strategies.
Read more:
There are components of self-insured claims data that Reclaim Health analyzes as part of its payment-integrity solution, including information about medical diagnoses and services, that contextualize each episode of care. This helps detect unusual patterns such as upcoding, duplicate billing or adjudication errors. AI machine learning optimization is paired with advocacy to claw back claims dollars on a post-payment basis for employers as well as their employees.
"There is pressure on brokers and consultants to demonstrate the actual financial value of benefits and benefit strategies that they're helping their clients with," Youssef acknowledges. "Employers are looking for demonstrated return of an investment, not methodologies – actual real dollars back, and that's why AI on its own for the purpose of detection is not enough. It has to lead into action."
This is part 2 of a series. Please find
