Introduction
Vagary is the reason lawyers have jobs. And they’re pretty good at sorting that vagary out—if you give them enough time and money. In Texas Medical Association v. HHS, they have neither. Today, the federal government has millions of healthcare insurance cases either in backlog or made ineligible under the No Surprises Act—so many that it’s conscripting private arbitrators, many without law degrees, to churn out the decisions. The reason for the backlog is the Act’s six-step balancing test, shot through with vague words and unmeasurable comparisons, around which the arbitrators must somehow develop an interpretive common law without the use of precedent (which the NSA has banned). The arbitrators can’t do this, so the federal government has been devising various methods of constructively removing the test from consideration. That’s led to a massive lawsuit, now on appeal before the Fifth Circuit for the third time, where both positions are losers: Either the appeals court jettisons formalism and rewrites the No Surprises Act itself, or it hews to the text and dooms healthcare litigation to perpetual mire.
The solution is a system that pairs—at an unprecedented speed—the nuance and specificity of ex post common law with the certainty and predictability of ex ante lawmaking. There’s only one tool for the job: an artificial intelligence that can rapidly distill bodies of common law into predictable, replicable codifications manifest in a fixed judicial output. We’ve got one; it’s called Arbitrus, and it’s the only way to cut formalism’s Gordian knot. Here’s how it would work in the healthcare context.
The reason law costs so much is that most of it is not written down. Indeed, half of the stuff is common law—which has no anchoring statute at all—and the rest is littered with vague statutory phrases whose meanings are a collective coin toss. In either case, you have to pay a lawyer to convince the judiciary what the vast majority of law should mean ex post. Unwritten law is expensive. It takes years. And AI will eliminate it.
On countless occasions, unwritten law has proven untenable for efficient governance. One of those occasions happens to be in front of the Fifth Circuit at the moment. The case is Texas Medical Association v. HHS—and it’s their third time hearing it, making this TMA v. HHS III. The premise is simple: The No Surprises Act (NSA) requires that private arbitrators, acting as deputized administrative law judges, apply a vague six-step balancing test. The test is confusing, and the arbitrators—who are paid a fixed fee per case and saddled with four times the caseload of the entire federal judiciary—simply don’t have the time to figure it out. So now the formalist Fifth Circuit is in a jam; either it holds firm to its textualist roots and dooms the arbitrators to an infinite backlog, or it reneges on its ethos and constructively amends the statute. Faced with two untenable routes, like Marshall in Marbury v. Madison, the en banc court may want to explore an Option C: supervised automation via an informed and unbiased arbitration engine. Option C is best for all sides—a single, definitive version of the six-step test applied the same way every time with unprecedented speed (and without sacrificing human supervision). There exists a less imperfect arbitrator—one who is partially automated—to get us out of this jam.
To be transparent and disclose our conflict of interest: the authors of this document have created such an unbiased, human-supervised, less imperfect arbitration engine that we call Arbitrus. We have applied for its certification by the CMS. We submit that Arbitrus is textualism’s fulfillment, and suggest that the answer to the intractable formalist quandaries of our day is not abandonment but innovation. Public policy for CMS IDR can be operationalized not only for the mutual benefit of insurers and hospitals, but also for the American people—and the Fifth Circuit has the chance to set our legal system on that path.
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