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762. Conversations that Matter: Developing an AI-Based Training Algorithm to Improve Provider Competence to Work with SGM Youth

BACKGROUND: Cultural incompetence hinders the engagement of sexual and gender minority (SGM) youth in the HIV prevention and care continuum. Improving providers’ cultural competence is critical to reducing HIV health inequities. Medical education lacks opportunity for practice and SGM perspectives....

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Autores principales: Hanshaw, Brady, Rachal, Laura, Gross, Jessica, Finacchio, Zachary, McElroy, Sarah, Noble, Skyler, Espada, Joshua, Shen, Thomas, Lina Rosengren, A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10678040/
http://dx.doi.org/10.1093/ofid/ofad500.823
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author Hanshaw, Brady
Rachal, Laura
Gross, Jessica
Finacchio, Zachary
McElroy, Sarah
Noble, Skyler
Espada, Joshua
Shen, Thomas
Lina Rosengren, A
author_facet Hanshaw, Brady
Rachal, Laura
Gross, Jessica
Finacchio, Zachary
McElroy, Sarah
Noble, Skyler
Espada, Joshua
Shen, Thomas
Lina Rosengren, A
author_sort Hanshaw, Brady
collection PubMed
description BACKGROUND: Cultural incompetence hinders the engagement of sexual and gender minority (SGM) youth in the HIV prevention and care continuum. Improving providers’ cultural competence is critical to reducing HIV health inequities. Medical education lacks opportunity for practice and SGM perspectives. We are developing an AI training app that uses virtual patients and a framework of educational and behavior change theory to increase provider clinical and communication competence with SGM youth. SGM youth and healthcare providers have unique knowledge about provider bias and stigma barriers to care, which can inform intervention content. METHODS: From Twitter's API, 42,213 Tweets were iteratively sampled by 11 SGM youth (the “YAB”) and analyzed thematically. These themes were combined with current clinical care guidelines for SGM youth to generate 111 stigma barriers to care agreed upon by the YAB by consensus. These themes were edited and ranked by importance by the YAB using a modified Delphi method. Using this content as a starting point, we held workshops with providers and the YAB to develop a comprehensive list of scenarios where providers noted deficient training in providing culturally competent care to SGM youth. Prioritized training scenarios selected from this list were developed into detailed scripts for AI virtual patient encounters. RESULTS: Using a model of social media mining informed by SGM youth and provider collaborative work, training scenarios were developed for integration into a GPT/AI provider training app. Scenarios address topics selected by SGM youth that are particularly salient to this population based on social media conversations around provider stigma and the YAB members’ lived experiences. Scenarios will progress from basic to advanced topics to train providers in culturally appropriate language and foster competent care for SGM youth. CONCLUSION: SGM youth have deep insight into their care needs and lived experiences of healthcare trauma. Understanding these care needs and building provider competence is critical to reduce HIV inequities among SGM youth. Our data provide new insight into how interactions between providers and SGM youth may improve. Future research will test the app’s effect on the clinical care behaviors of providers in a randomized control trial. DISCLOSURES: All Authors: No reported disclosures
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spelling pubmed-106780402023-11-27 762. Conversations that Matter: Developing an AI-Based Training Algorithm to Improve Provider Competence to Work with SGM Youth Hanshaw, Brady Rachal, Laura Gross, Jessica Finacchio, Zachary McElroy, Sarah Noble, Skyler Espada, Joshua Shen, Thomas Lina Rosengren, A Open Forum Infect Dis Abstract BACKGROUND: Cultural incompetence hinders the engagement of sexual and gender minority (SGM) youth in the HIV prevention and care continuum. Improving providers’ cultural competence is critical to reducing HIV health inequities. Medical education lacks opportunity for practice and SGM perspectives. We are developing an AI training app that uses virtual patients and a framework of educational and behavior change theory to increase provider clinical and communication competence with SGM youth. SGM youth and healthcare providers have unique knowledge about provider bias and stigma barriers to care, which can inform intervention content. METHODS: From Twitter's API, 42,213 Tweets were iteratively sampled by 11 SGM youth (the “YAB”) and analyzed thematically. These themes were combined with current clinical care guidelines for SGM youth to generate 111 stigma barriers to care agreed upon by the YAB by consensus. These themes were edited and ranked by importance by the YAB using a modified Delphi method. Using this content as a starting point, we held workshops with providers and the YAB to develop a comprehensive list of scenarios where providers noted deficient training in providing culturally competent care to SGM youth. Prioritized training scenarios selected from this list were developed into detailed scripts for AI virtual patient encounters. RESULTS: Using a model of social media mining informed by SGM youth and provider collaborative work, training scenarios were developed for integration into a GPT/AI provider training app. Scenarios address topics selected by SGM youth that are particularly salient to this population based on social media conversations around provider stigma and the YAB members’ lived experiences. Scenarios will progress from basic to advanced topics to train providers in culturally appropriate language and foster competent care for SGM youth. CONCLUSION: SGM youth have deep insight into their care needs and lived experiences of healthcare trauma. Understanding these care needs and building provider competence is critical to reduce HIV inequities among SGM youth. Our data provide new insight into how interactions between providers and SGM youth may improve. Future research will test the app’s effect on the clinical care behaviors of providers in a randomized control trial. DISCLOSURES: All Authors: No reported disclosures Oxford University Press 2023-11-27 /pmc/articles/PMC10678040/ http://dx.doi.org/10.1093/ofid/ofad500.823 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Abstract
Hanshaw, Brady
Rachal, Laura
Gross, Jessica
Finacchio, Zachary
McElroy, Sarah
Noble, Skyler
Espada, Joshua
Shen, Thomas
Lina Rosengren, A
762. Conversations that Matter: Developing an AI-Based Training Algorithm to Improve Provider Competence to Work with SGM Youth
title 762. Conversations that Matter: Developing an AI-Based Training Algorithm to Improve Provider Competence to Work with SGM Youth
title_full 762. Conversations that Matter: Developing an AI-Based Training Algorithm to Improve Provider Competence to Work with SGM Youth
title_fullStr 762. Conversations that Matter: Developing an AI-Based Training Algorithm to Improve Provider Competence to Work with SGM Youth
title_full_unstemmed 762. Conversations that Matter: Developing an AI-Based Training Algorithm to Improve Provider Competence to Work with SGM Youth
title_short 762. Conversations that Matter: Developing an AI-Based Training Algorithm to Improve Provider Competence to Work with SGM Youth
title_sort 762. conversations that matter: developing an ai-based training algorithm to improve provider competence to work with sgm youth
topic Abstract
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10678040/
http://dx.doi.org/10.1093/ofid/ofad500.823
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