AI Pricing Tools in Senior Care: The Antitrust Risks You Need to Manage Now (www.jdsupra.com)

Senior living and nursing home operators are increasingly using third‑party “revenue management” software to set private‑pay rates, discounts and concessions — and recent litigation and enforcement actions show that can create antitrust exposure even without direct talks between competitors. Plaintiffs in Duffy v. Yardi allege a pricing platform collected nonpublic competitor data and used algorithms to steer landlords toward aligned rents; courts have allowed per se conspiracy claims to proceed there. By contrast, Gibson v. Cendyn (hotel pricing) was dismissed where plaintiffs couldn’t show confidential data sharing or an agreement to follow vendor recommendations, signaling that mere parallel use of software isn’t enough. Regulators and settlements (e.g., RealPage-related actions) highlight theories like information exchange and vendor‑facilitated coordinated outcomes that apply across sectors pricing private‑pay services, including assisted living and memory care. For AI/ML teams, compliance, and operators this means focusing on data provenance, governance and vendor controls: ban ingestion or redistribution of nonpublic competitor inputs; disable cross‑client signaling and any dashboard features that enable market‑level inference; require heavily aggregated and time‑lagged benchmarking; and keep humans explicitly in the loop — document independent factors when deviating from recommendations. Preclear high‑risk configurations, audit vendors, and build a record that pricing decisions remain independent. Algorithmic tools aren’t per se unlawful, but design, data flows and contractual safeguards now determine antitrust risk.