As artificial intelligence (AI) continues to permeate the health care industry (and every other part of American life), the industry should be attentive to the risk of anticompetitive conduct arising from reliance on AI and cognizant of the watchful eyes of antitrust enforcers, including the Department of Justice (DOJ) and the Federal Trade Commission (FTC). Last Friday, the DOJ underlined these concerns when it formed the Health Care Monopolies and Collusion (HCMC) task force “to elevate the importance of health care antitrust enforcement.”1 Today, we discuss the application of antitrust law to the use of AI in health care, including recent allegations of algorithmic price fixing in the setting of certain out-of-network rates.
On April 7, 2024, a New York Times (NYT) report detailed claims that several large health plans and third-party administrators (TPAs) rely on the same company’s algorithmic tools to set the amount the plan will offer to out-of-network providers, which impacts the amount of the plan member’s remaining out-of-pocket cost for the care.2 Of course, out-of-network charges have been the subject of intense focus at the state and federal level, including most prominently in the federal No Surprises Act (NSA). The NSA prohibits out-of-network providers from charging in excess of the median in-network charge in the relevant geographic area for out-of-network emergency care and certain out-of-network services provided at in-network hospitals.3 For more information about the NSA, please see our extensive coverage or contact the authors of this article. According to the NYT report, the charges at issue were not covered by the NSA.
In response to the NYT report, Senator Amy Klobuchar of Minnesota wrote to the heads of the DOJ Antitrust Division and the FTC on April 29, 2024 and asked them to investigate “the use of algorithms that collect and process data in the out-of-network insurance payment industry to determine payments for physicians and out-of-pocket costs for patients to determine whether any of this conduct violates the law.”4 Senator Klobuchar emphasized that algorithms should not be used “to allow competitors to collude to make healthcare more costly for patients.”
Companies in the health care industry should follow these developments closely because any DOJ HCMC task force or FTC investigation or enforcement action will provide important guidance on how federal and state antitrust enforcers will treat reliance on AI for making competitively sensitive and important decisions, such as pricing, while operating in the highly‑regulated health care space.
Antitrust Basics
In antitrust law, price fixing is an agreement among competitors to raise, fix or otherwise maintain the price of their respective products or services.5 Price fixing is per se illegal without any inquiry into the reasonableness of the particular agreement.6 Proving a price-fixing agreement between competitors generally requires evidence of a common understanding or “meeting of the minds.”7
Price-fixing in the Era of Artificial Intelligence
“Price fixing by algorithm is still price fixing,” as the FTC recently pointed out.8 Competitors who agree to use an AI-powered algorithm to set their prices, sometimes referred to as “algorithmic collusion,” could run afoul of the antitrust laws. For example, in 2015, the Antitrust Division secured a criminal guilty plea from a seller of wall posters on Amazon after he and his “co-conspirators adopted specific pricing algorithms for the sale of certain posters with the goal of coordinating changes to their respective prices and wrote computer code that instructed algorithm-based software to set prices in conformity with this agreement.”9
In 2017, the FTC and DOJ submitted a position paper to the Organization for Economic Co‑operation and Development, explaining that “if competing firms each entered into separate agreements with a single firm (for instance a platform) to use a particular pricing algorithm, and the evidence showed they did so with the common understanding that all of the other competitors would use the identical algorithm, that evidence could be used to prove an agreement among the competitors that violates U.S. antitrust law.”10
Consistent with this policy statement, in November 2023, DOJ submitted a Statement of Interest in a private class action asserting that because “competitors’ joint use of common algorithms can remove independent decision making” … “[a]lgorithmic price fixing must therefore be subject to the same condemnation as other price-fixing schemes.”11 In DOJ’s view, price fixing through joint use of a software algorithm is the same as “sharing information through email, fax machine or face-to-face conversation.”
More recently, in March 2024, DOJ and FTC submitted Statements of Interest in two private class actions alleging algorithmic price fixing by competitors in the business of renting apartments and hotel rooms. The antitrust agencies contend that “joint delegation of pricing recommendations to a common algorithm … alter[s] the starting point of prices” and that “such agreements among competitors are analogous to agreements to fix list prices—distorting the competitive pricing process that the per se rule protects.”12
The concerns highlighted by DOJ, FTC, the NYT article and Senator Klobuchar are already playing out in federal antitrust litigation against MultiPlan. Specifically, in August 2023, a large hospital network sued Multiplan for price fixing, and in April and May 2024, two antitrust class actions were filed against Multiplan and numerous insurers alleging the operation of a price‑fixing cartel and monopolization of the market for commercial health insurance repricing.13
Also, in response to the NYT report, the American Hospital Association urged the U.S. Department of Labor to investigate MultiPlan and “hold companies like MultiPlan and its corporate commercial insurer partners to account for these unconscionable practices, distorted incentives, potential violations of ERISA, and ultimately, harms to American patients and employees.”14
While the opaque nature of AI processes makes it challenging for antitrust enforcers to prove a computer program (which cannot testify) engaged in price fixing, FTC and DOJ believe that human competitors who agree to use the same AI-powered solution to set prices may improperly collude in violation of the antitrust laws. Moreover, even if the conduct does not violate the antitrust laws, it may be actionable under state unfair competition laws.
Takeaways for Health Care Industry Participants
It is crucial that, as the health care industry increasingly embraces artificial intelligence to make operating health care businesses more efficient, providers, payers and others must carefully consider whether reliance on AI systems for core functions like pricing, billing and paying claims creates a risk of liability under existing legal schemes such as antitrust and unfair competition laws. This analysis is just one aspect of the overall governance program that health care entities should implement as their use of AI, either internally or through vendors, continues to ramp-up. For more information about AI governance or to consult with experts regarding the risk of liability for the use of AI in health care settings, please contact the authors of this report.
The authors of this article are not involved in the disputes described in the NYT article and the Klobuchar letter, and it may well be that the conduct described is not in violation of the law. In addition, as the NYT article notes, the health plans and TPAs contend that they used algorithmic pricing to help them respond to alleged price gouging by private equity-backed medical practices and health facilities.
Another takeaway is that Congress should consider expanding the NSA to include additional types of out-of-network claims to avoid the human toll reflected in the NYT article. Where the NSA applies, the amount owed by the member for out-of-network care is fixed by statute and payers and providers must either agree on the amount of the out-of-network reimbursement or resolve the dispute through the federal Independent Dispute Resolution process, a new form of federal arbitration established by the NSA.
1 Press Release, U.S Dep’t of Justice, Assistant Attorney Jonathan Kanter Announces Task Force on Health Care Monopolies and Collusion (Apr. 9, 2024), available at https://www.justice.gov/opa/pr/assistant-attorney-general-jonathan-kanter-announces-task-force-health-care-monopolies-and.
2 Chris Hamby, Insurers Reap Hidden Fees by Slashing Payments. You May Get the Bill., NY Times (Apr. 7, 2024), https://www.nytimes.com/2024/04/07/us/health-insurance-medical-bills.html (discussing alleged use of tools provided by MultiPlan).
3 Ending Surprise Medical Bills, https://www.cms.gov/nosurprises.
4 Ltr., Sen. A. Klobuchar to AAG J. Kanter and FTC Chair L. Khan (April 29, 2024), https://www.klobuchar.senate.gov/public/_cache/files/4/4/4463fdf7-457e-4e48-b885-9dca394c57d4/7F84E808973057BD75668746A378A06B.4.29.2024-letter-to-doj-ftc-re-multiplan-insurance-payments.pdf.
5 E.g., Arizona v. Maricopa Cty. Med. Soc’y, 457 U.S. 332, 343-44 (1982).
6 E.g., Verizon Communications v. Law Offices of Curtis V. Trinko, 540 U.S. 398, 408 (2004) (price-fixing among direct competitors is considered the “supreme evil of antitrust”); U.S. v. Socony-Vacuum Oil Co., 310 U.S. 150, 226 n.59 (1940) (“Whatever economic justification particular price-fixing agreements may be thought to have, the law does not permit an inquiry into their reasonableness. They are all banned because of their actual or potential threat to the central nervous system of the economy.”).
7 E.g., Monsanto Co. v. Spray–Rite Service Corp., 465 U.S. 752, 764 (1984) (colluding competitors must be shown to have “unity of purpose or a common design and understanding or a meeting of minds” or “a conscious commitment to a common scheme.”).
8 Business blog, Fed. Trade Comm’n, Price fixing by algorithm is still price fixing (Mar. 1, 2024), available at https://www.ftc.gov/business-guidance/blog/2024/03/price-fixing-algorithm-still-price-fixing.
9 Press release, Dep’t of Justice, Former E-Commerce Executive Charged with Price Fixing in the Antitrust Division's First Online Marketplace Prosecution (Apr. 6, 2015), available at https://www.justice.gov/opa/pr/former-e-commerce-executive-charged-price-fixing-antitrust-divisions-first-online-marketplace.
10 Algorithms and Collusion - Note by the United States, at ¶17 (May 26, 2017), available at https://one.oecd.org/document/DAF/COMP/WD(2017)41/en/pdf.
11 Statement of Interest of the United States at 2, In re Realpage, Rental Software Antitrust Litig. (No. II), No. 3:23-MD-3071 (M.D. Tenn. Nov. 15, 2023), Dkt. 627, available at https://www.justice.gov/d9/2023-11/418053.pdf.
12 Statement of Interest of the United States at 6, Duffy v. Yardi Systems, Inc., No. 2:23-cv-01391 (W.D. Wash. Mar. 1, 2024), Dkt. 149, available at https://www.ftc.gov/system/files/ftc_gov/pdf/YardiSOI-filed%28withattachments%29_0.pdf; see also Statement of Interest of the United States at 4-5, Cornish-Adebiyi v. Caesars Entertainment, Inc., No. 1:23-cv-02536 (D.N.J. Mar. 28, 2024) (“Concerted action can take many different forms, including competitors’ jointly delegating key aspects of decisionmaking to a common entity, such as an algorithm provider.”), Dkt. 96, available at https://www.justice.gov/opa/media/1345721/dl?inline.
13 E.g., Complaint, Live Well Chiropractic PLLC v. MultiPlan, Inc., No. 1:24-cv-03680 (N.D. Ill. May 6, 2024), Dkt. 1; Complaint, Allegiance Health Management, Inc. v. MultiPlan, Inc., No. 1:24-cv-03223 (N.D. Ill. Apr. 22, 2024); Complaint, Adventist Health Sys. Sunbelt Healthcare Corp. v. MultiPlan, Inc., No. 1:23-cv-07031 (S.D.N.Y. Aug. 9, 2023), Dkt. 1.
14 Ltr. from AHA Pres. R. Pollack to Acting Sec. of Labor J. Su (Apr. 9, 2024), available at https://www.aha.org/lettercomment/2024-04-09-following-nyt-investigation-aha-urges-dol-investigate-actions-multiplan-and-commercial-insurers.
