This Post is the latest in the Hub and Spoke’s continuing series on Pricing Algorithms and Health Care. You can find the Hub and Spoke’s previous coverage here, here, here, here, here, here, and here.
Another federal district court has weighed in on the legality of competitors using a common pricing algorithm, and the result provides a new gloss on how courts may evaluate future claims of algorithmic collusion, regardless of industry.
In In re MultiPlan, a district court in Chicago declined to dismiss claims of algorithmic collusion among health insurers and others.[1] The plaintiffs in the case—doctors and other healthcare providers—allege that the insurers, together with a company called MultiPlan, have conspired to fix reimbursements paid to providers using MultiPlan’s algorithm, called Data iSight. The algorithm is used by insurers and other third-party payors to offer reimbursements to healthcare providers for out-of-network medical services delivered to patients. At its core, the plaintiffs’ case accuses MultiPlan of coordinating a conspiracy among payors to suppress those reimbursements to healthcare providers.
In denying the defendants’ motions to dismiss, the MultiPlan court tracked new territory in how courts evaluate claims based of algorithmic collusion. Prior decisions largely have turned on whether the algorithm in question facilitated the sharing of sensitive confidential information among its users, thus suggesting a conspiracy.[2] But in the case of Data iSight, the plaintiffs’ allegations pointed in the other direction—to documents produced by MultiPlan showing that the Data iSight algorithm relied on publicly available data, which, according to the Court, may not raise the same competitive concerns.
Nonetheless, the court rejected the defendants’ motions to dismiss, holding that other allegations demonstrated that Multiplan played larger information-intermediary role that justified allowing the plaintiffs’ claims to move forward.
[T]he crux of the plaintiffs’ information sharing allegations goes beyond MultiPlan’s algorithm. As the class action plaintiffs put it, MultiPlan is alleged not just to have “hidden behind its algorithm,” but also to have to have “played an active role as a go-between” for third-party payors.
The court held that this broader role in the sharing of confidential information and coordination of pricing was a plus factor suggestive of larger conspiracy among the users of MultiPlan’s algorithm:
These allegations indicate that third-party payors know and effectuate MultiPlan’s communication of competitively sensitive pricing information from one third-party payor to another despite a third-party payor’s self-interest in keeping potentially detrimental price information private.
Thus, even where an algorithm provider, like MultiPlan, does not base the algorithm on confidential information, a court may still find allegations supporting a conspiracy if the provider is engaging in a broader “consulting” role, assisting its clients in formulating pricing strategy based on competitor information.
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In other pricing-algorithm news: this week, Congress—in an abrupt about-face—firmly rejected a legislative provision that would have effectively prohibited states from regulating pricing algorithms and other technologies based on artificial intelligence, or AI.
As recently as Sunday, the Trump Administration’s “Big Beautiful Bill” had included a provision that would have barred states from regulating any software that relied on AI for a 10-year period. The provision likely would have undone or blocked any legislation at the state or local level that regulates pricing algorithms.
But early Tuesday morning, the Senate roundly rejected the provision on a bipartisan basis, voting 99-1 to remove it from the larger bill. The change allows states to continue to experiment with regulations concerning AI and its innumerable applications to modern life, including within pricing algorithms.
[1] 24-cv-6795 (N.D. Ill. June 3, 2025), ECF No. 428.
[2] Compare In re RealPage, Inc., Rental Software Antitrust Litig., 709 F. Supp. 3d 478, 510 (M.D. Tenn. 2023) (denying motion to dismiss and stating that “the Court finds that the . . . most persuasive evidence of horizontal agreement is the simple undisputed fact that each [defendant] provided RealPage it proprietary commercial data, knowing that RealPage would require the same from its horizontal competitors and use all of that data to recommend rental prices to its competitors.”), with Gibson v. Cendyn Grp., LLC, No 3-cv-140, 2024 WL 2060256, at *16 (D. Nev. May 8, 2024 (granting motion to dismiss and holding that “Plaintiffs only allege that [defendants] are getting public data about other [defendants] by using [the algorithms], and that does not suggest collusion.”).
