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A dispatch from HIMSS, and Trump's chilling effect on health AI

Mar 06, 2025 - statnews.com
The article provides an overview of key discussions and announcements from the Healthcare Information and Management Systems Society (HIMSS) conference, focusing on health data exchange and interoperability. Major themes include the uncertainty surrounding the future of the government-supported health data exchange framework TEFCA, concerns about trust in the Carequality network following a legal dispute, and the impending CMS-0057 interoperability rule requiring the adoption of FHIR APIs by 2027. Additionally, Aneesh Chopra proposed a "Neighborhood Watch" initiative to improve vendor accountability in data exchange, and there are concerns about the Trump administration's impact on AI research due to restrictions on diversity-related terms.

The article also highlights the launch of AdvocateMH, a new mental health startup by former Cerebral CEO David Mou and Tom Insel, aiming to improve mental health care standards. The Coalition for Health AI announced a model card registry to enhance transparency in algorithm development, and the Peterson Health Technology Institute plans to evaluate digital health solutions for opioid use disorder. Several companies, including Microsoft and Abridge, made announcements related to AI and clinical documentation at the conference.

Key takeaways:

  • Uncertainty surrounds the future of TEFCA and its funding under a new administration, causing anxiety about its continued support for health data exchange.
  • The CMS-0057 interoperability rule requires insurers to adopt FHIR APIs by 2027, aiming to streamline patient data exchange and reduce reliance on outdated methods like faxing.
  • AdvocateMH, a new mental health startup by former Cerebral CEO David Mou and Tom Insel, aims to improve mental health care quality by using licensed clinicians to guide patients to appropriate services.
  • The Coalition for Health AI launched a model card registry to enhance transparency and help health systems evaluate algorithmic products, aligning with ONC transparency requirements.
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