JOURNAL ARTICLE

Malpractice Liability Exposure and the Sports Medicine Team Physician: Caring for Professional Athletes in the National Football League, Major League Baseball, and National Hockey League.

  • Published In: American Journal of Sports Medicine, 2025, v. 53, n. 2. P. 261 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Pang, Michael; Hasan, Sayyida S.; Woo, Joshua J.; Olsen, Reena J.; Ramkumar, Prem N. 3 of 3

Abstract

This article focuses on the malpractice liability exposure faced by orthopaedic surgeons caring for elite athletes in the National Football League (NFL), Major League Baseball (MLB), and National Hockey League (NHL). Analyzing 4,419 player contracts from the 2022-2023 season, the study models risk ratios based on typical malpractice insurance coverage of 1 million and 3 million US dollars (USD) per claim, revealing that standard coverage is insufficient to protect physicians from potential financial loss, especially for high-value player positions such as quarterbacks in the NFL, right fielders in the MLB, and centers in the NHL. To adequately cover 95% of players, malpractice policies would need to range from approximately 52.6 million to 108.1 million USD depending on the league. The findings underscore the need for enhanced malpractice coverage and coordinated risk-sharing strategies among sports franchises, hospitals, players, agents, and physicians to safeguard both medical providers and athlete care.

Additional Information

  • Source:American Journal of Sports Medicine. 2025/02, Vol. 53, Issue 2, p261
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2025
  • ISSN:0363-5465
  • DOI:10.1177/03635465241306742
  • Accession Number:182791662
  • Copyright Statement:Copyright of American Journal of Sports Medicine is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.