5 Key Takeaways from CEO Warris Bokhari on The Real Eisman Playbook

Health insurance is supposed to make medical care predictable. But for tens of millions of Americans each year, it becomes the opposite: a source of uncertainty, delay, and financial shock.

In a recent episode of The Real Eisman Playbook, Claimable CEO Warris Bokhari joined host Steve Eisman for a wide-ranging conversation about health insurance denial rates, what’s driving them, and why the denial-and-appeal process often feels stacked against patients and providers. This post recaps the biggest themes from the episode – and what they mean for both healthcare workers and patients navigating care.

Note on sourcing: the statistics and examples below are presented as they were discussed in the episode and attributed accordingly.

What’s inside – and what surprised us

A few observations from Bokhari stood out, highlighting how the system of denials is complex and layered – going beyond just denying care.

• The AI denial problem goes deeper than it looks. Bokhari’s critique reveals that insurers can apply stale or poorly maintained policies at machine speed with AI, scaling old criteria and mismatched reviews faster than patients can respond.
  
  
• The system relies on you not fighting back. Bokhari argued that denial economics work because appeal rates stay tiny. If more people appeal, insurers face real cost and operational friction – quickly.
  
  
• Administrative delay is a strategy, not a side effect. Beyond initial denials, Bokhari described time-reset tactics that push payment further out and increase paperwork burden – especially crushing for small practices.

What are health insurance denial rates – and why do they matter?

Health insurance denial rates refer to the percentage of medical claims that are initially denied by an insurer. A denial can mean “not medically necessary,” “not on formulary,” “out of network,” “missing documentation,” or “needs step therapy,” among other reasons. Importantly, “denied” often doesn’t mean the care is inappropriate – it can mean the plan’s rules, paperwork, or policy logic blocked payment.

In the episode, Bokhari points to denial rates in the mid-teens on average (he references ~17%; other figures discussed vary by segment), and he highlights the scale effect: the U.S. processes roughly 5 billion claims annually, so a ~17% denial rate implies roughly 850 million denials in a year – impacting an estimated 70–90 million Americans.

5 key takeaways from the episode

1) Denials are a scale problem, not a rare exception

One of the strongest points from the conversation was sheer magnitude. Bokhari cited the U.S. processing roughly 5 billion claims annually and described how even a “mid-teens” denial rate becomes an enormous number of denied claims in practice – around 850 million a year – impacting an estimated 70–90 million Americans.

But his argument wasn’t limited to outright denials. He described a broader category of coverage friction that functions like denial in practice, interrupting care and payment even when the underlying treatment may be appropriate. These “deny-by-delay” tactics include:

• Shift delay / prompt-pay resets: insurers can request new information or submit an “edit” that resets the clock on when payment is due – extending the float and increasing paperwork.‍
• Predetermination: essentially prior authorization under another name – an added gate that may not guarantee coverage but still slows everything down.
• Clawbacks: care is approved and delivered, then months later the insurer reverses course and demands money back – creating major liquidity risk for providers.

Why it matters: when friction happens at scale, it becomes a population-level access barrier, not just an individual inconvenience. The result is delays for patients, administrative drag for providers, and a system that quietly shifts “care access” from clinical fit to coverage navigation.

2) Appeals can work – yet almost nobody files them

A core tension in the episode: denials may be common, but successful appeals can also be common. Bokhari referenced public reporting suggesting roughly ~50% of appealed denials can be overturned, while describing much higher outcomes in certain categories (including an anecdote from an insurance executive claiming extremely high overturn rates, and Claimable’s experience in specific areas).

Then came the headline problem: fewer than 1% of denied cases are appealed – because the process is confusing, time-consuming, and hard to manage while sick.

Why it matters: low appeal volume functions like “silent acceptance.” It allows friction to become a form of de facto cost control – without necessarily reflecting whether care is clinically appropriate.

3) AI is accelerating decisions—and scaling policy mistakes

The episode repeatedly returned to speed. Bokhari described claim decisions happening in seconds (he cites examples as fast as ~1.2 seconds) and argued insurers increasingly use automation to deny quickly – sometimes with logic that is outdated, inconsistently applied, or poorly matched to a patient’s situation.

His point wasn’t just “automation is bad.” It’s that bad policy applied quickly becomes a multiplier: the faster it runs, the more people it hits, and the more downstream rework it creates. 

Why it matters: faster denials don’t just arrive faster – they create cascading work: more paperwork, more resubmissions, more calls, more delays to treatment. That variability hits hardest in specialty and high-stakes care where timing matters.

4) Denial rates are tied to incentives—especially short-term risk vs. long-term illness

Bokhari offered a blunt framing: health insurance is often treated like a short-term financial product, while many medical needs are long-term realities. On the commercial side, he argued insurers may carry someone for roughly 18–24 months on average – job changes and plan switching—creating misaligned incentives when care is expensive and benefits accrue over years.

That incentive mismatch becomes especially acute as treatments improve (and cost more), including in oncology – something Bokhari says is increasingly showing up in what he’s seeing.

Why it matters: when incentives reward delay or denial, “coverage” becomes unpredictable at the point of need. Patients face uncertainty. Providers and hospitals carry working-capital strain. And employers – who ultimately fund a lot of this – often don’t realize how plans actually operate until a catastrophic case forces visibility.

5) If appeals rise from <1% to ~3%, the denial model starts to break

This was one of Bokhari’s most distinctive system-level claims: the denial/appeal equilibrium depends on appeals staying rare. He described appeals as operationally expensive for insurers (he cites internal conversations suggesting roughly hundreds of dollars per appeal to process, potentially far more when claim files or escalation are involved).

His broader argument: if you move appeals from less than 1% of denials to even ~3%, insurers can’t treat denials as cheap friction anymore. Processing burden rises, economics change, and blanket “deny fast” strategies become harder to sustain.

Why it matters: this reframes appeals as more than individual advocacy. If enough people appeal, it becomes a system lever – changing incentives, not just single outcomes.

Watch the full conversation

If you want the full context—including the personal stories, the incentive mechanics, and the discussion of how denial tactics spill into provider finance and consolidation—watch the episode here:

The Real Eisman Playbook featuring Claimable CEO Warris Bokhari (YouTube)

Closing thought

A denial can feel like a verdict – especially when it arrives quickly and without clear explanation. The episode’s larger point is that denial rates aren’t just an individual frustration; they’re a structural feature of how coverage operates today.

And when more people understand that appeals can work – and that higher appeal volume changes incentives—the system’s “deny fast, few fight back” equilibrium starts to weaken.

If you’ve faced a denial: you’re not alone—and you’re not powerless. 

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