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Using LLMs to Help Prevent Pediatric Data Disclosures

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Manage episode 419168337 series 2797294
Content provided by Xtelligent Healthcare Media. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Xtelligent Healthcare Media or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Unintentional disclosures of adolescents’ protected health information are major privacy and security concerns that providers must navigate. Natalie Pageler, MD, chief health informatics officer and division chief of pediatric clinical informatics at Stanford Medicine Children’s Health, discusses how the use of large language models (LLMs) could help prevent these disclosures by identifying confidential content in clinical notes.

  continue reading

117 episodes

Artwork
iconShare
 
Manage episode 419168337 series 2797294
Content provided by Xtelligent Healthcare Media. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Xtelligent Healthcare Media or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Unintentional disclosures of adolescents’ protected health information are major privacy and security concerns that providers must navigate. Natalie Pageler, MD, chief health informatics officer and division chief of pediatric clinical informatics at Stanford Medicine Children’s Health, discusses how the use of large language models (LLMs) could help prevent these disclosures by identifying confidential content in clinical notes.

  continue reading

117 episodes

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