Disparities in seizure outcomes revealed by large language models

Objective Large-language models (LLMs) in healthcare have the potential to propagate existing biases or introduce new ones. For people with epilepsy, social determinants of health are associated with disparities in access to care, but their impact on seizure outcomes among those with access to specialty care remains unclear. Here we (1) evaluated our validated, epilepsy-specific LLM for intrinsic bias, and (2) used LLM-extracted seizure outcomes to test the hypothesis that different demographic groups have different seizure outcomes. Methods First, we tested our LLM for intrinsic bias in the form of differential performance in demographic groups by race, ethnicity, sex, income, and health insurance in manually annotated notes. Next, we used LLM-classified seizure freedom at each office visit to test for outcome disparities in the same demographic groups, using univariable and multivariable analyses. Results We analyzed 84,675 clinic visits from 25,612 patients seen at our epilepsy center 2005-2022. We found no differences in the accuracy, or positive or negative class balance of outcome classifications across demographic groups. Multivariable analysis indicated worse seizure outcomes for female patients (OR 1.33, p = 3x10-8), those with public insurance (OR 1.53, p = 2x10-13), and those from lower-income zip codes (OR ≥ 1.22, p ≤ 6.6x10-3). Black patients had worse outcomes than White patients in univariable but not multivariable analysis (OR 1.03, p = 0.66). Significance We found no evidence that our LLM was intrinsically biased against any demographic group. Seizure freedom extracted by LLM revealed disparities in seizure outcomes across several demographic groups. These findings highlight the critical need to reduce disparities in the care of people with epilepsy. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was funded by the National Institute of Neurological Disorders and Stroke DP1NS122038; by the National Institutes of Health R01NS125137; the Mirowski Family Foundation; by contributions from Neil and Barbara Smit; and by contributions from Jonathan and Bonnie Rothberg. WKSO was supported by the National Science Foundation Research Grant Fellowship DGE-1845298. RSG was supported by the National Institute of Neurological Disorders and Stroke T32NS091006. CAE was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health Award Number K23NS121520; by the American Academy of Neurology Susan S. Spencer Clinical Research Training Scholarship; and by the Mirowski Family Foundation. DR was partially funded by the Office of Naval Research Contract N00014-19-1-2620. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This research was approved by the Institutional Review Board of the University of Pennsylvania with a Waiver of Informed Consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Our NLP models are available on the Hugging Face hub at , and our code is available on GitHub at [https://github.com/penn-cnt/NLP\_Disparities\_in\_Seizure\_Freedom][1]. We do not make our data available to protect patient privacy. [1]: https://github.com/penn-cnt/NLP_Disparities_in_Seizure_Freedom