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Appeals Court Ruling Permits "Disproportionate Share Payment" Reopenings and Authorizes "Mandamus" Review of Medicare Action

Client Alert | 1 min read | 07.28.01

In an important pro-hospital ruling secured by Crowell & Moring partner Bob Roth, the United States Court of Appeals for the D.C. Circuit has required CMS to permit the plaintiff hospitals to reopen Medicare cost reports to effectuate the disproportionate share hospital ("DSH") policy change. On July 27, 2001, the Court decided Monmouth Medical Center v. Thompson. The Court overturned the part of Ruling 97-2 that barred hospitals from reopening cost reports to recalculate the DSH payment in accordance with the new methodology in the 97-2 ruling. Also important for its implications for future efforts to secure judicial review of Medicare agency action, the Court reversed the lower court's dismissal of the case for lack of subject matter jurisdiction, finding that the lower court should have exercised mandamus jurisdiction to order the relief sought.

The Monmouth decision may be helpful for other hospitals, particularly those with potential claims relating to Notices of Program Reimbursement issued on or after February 27, 1994. Hospitals that qualify for relief under this decision may be able to proceed directly to court in the District of Columbia without having to exhaust administrative remedies. It is not yet clear what, if any, further appellate review the government might seek.

Insights

Client Alert | 9 min read | 10.21.26

Artificial Intelligence in Life Sciences: Reliability of AI-Generated Evidence in Drug Discovery and Development

The life sciences sector is uniquely vulnerable to the inherent risks of AI-generated evidence in drug development and discovery. This client alert focuses on five core challenges: hallucinations, skewed predictions from unrepresentative training data, the opacity of AI reasoning, input sensitivity that undermines reproducibility, and adversarial manipulation of training data. To address these risks, companies should use experimental validation and human oversight as the common legal and regulatory safety net. This entails among other measures auditing AI outputs, characterizing training data, and structural oversight. Companies also should prepare for evolving frameworks and guidance from the U.S. Food and Drug Administration (FDA) and the European Union (EU) that address use of AI in drug discovery and development .  ...