AI safety evaluation in an underrepresented population: real-world performance of clinical decision support and frontier language models on Medicaid patient messaging triage

Abstract

Studies of artificial intelligence tools used in patient triage have largely involved academic medical center cohorts, scripted patient-actor scenarios, or knowledge benchmarks. Populations that may rely on such tools due to constrained access to in-person care, including Medicaid patients, have been less fully evaluated. To compare combinations of safety guardrails added to artificial intelligence tools for triage of patient-initiated text messages in a multi-state Medicaid populat

Categories

@article{basu2026ai,
  title = {{AI safety evaluation in an underrepresented population: real-world performance of clinical decision support and frontier language models on Medicaid patient messaging triage}},
  author = {Sanjay Basu and Sadiq Y. Patel and Parth Sheth and Bernardo Arevalo and Jeremy Schifberg and John Morgan and Rajaie Batniji},
  year = {2026},
  month = aug,
  journal = {BMC Medical Informatics and Decision Making},
  doi = {10.1186/s12911-026-03763-z},
  url = {https://www.semanticscholar.org/paper/fee3e808aed590c7fbe755d7dd4e9d3e5acf427e}
}