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COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots
BACKGROUND: Disparities in COVID-19 information and vaccine access have emerged during the pandemic. Individuals from historically excluded communities (eg, Black and Latin American) experience disproportionately negative health outcomes related to COVID-19. Community gaps in COVID-19 education, soc...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879317/ https://www.ncbi.nlm.nih.gov/pubmed/36630649 http://dx.doi.org/10.2196/39045 |
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author | Perez-Ramos, Jose G Leon-Thomas, Mariela Smith, Sabrina L Silverman, Laura Perez-Torres, Claudia Hall, Wyatte C Iadarola, Suzannah |
author_facet | Perez-Ramos, Jose G Leon-Thomas, Mariela Smith, Sabrina L Silverman, Laura Perez-Torres, Claudia Hall, Wyatte C Iadarola, Suzannah |
author_sort | Perez-Ramos, Jose G |
collection | PubMed |
description | BACKGROUND: Disparities in COVID-19 information and vaccine access have emerged during the pandemic. Individuals from historically excluded communities (eg, Black and Latin American) experience disproportionately negative health outcomes related to COVID-19. Community gaps in COVID-19 education, social, and health care services (including vaccines) should be prioritized as a critical effort to end the pandemic. Misinformation created by the politicization of COVID-19 and related public health measures has magnified the pandemic’s challenges, including access to health care, vaccination and testing efforts, as well as personal protective equipment. Information and Communication Technology (ICT) has been demonstrated to reduce the gaps of marginalization in education and access among communities. Chatbots are an increasingly present example of ICTs, particularly in health care and in relation to the COVID-19 pandemic. OBJECTIVE: This project aimed to (1) follow an inclusive and theoretically driven design process to develop and test a COVID-19 information ICT bilingual (English and Spanish) chatbot tool named “Ana” and (2) characterize and evaluate user experiences of these innovative technologies. METHODS: Ana was developed following a multitheoretical framework, and the project team was comprised of public health experts, behavioral scientists, community members, and medical team. A total of 7 iterations of ß chatbots were tested, and a total of 22 ß testers participated in this process. Content was curated primarily to provide users with factual answers to common questions about COVID-19. To ensure relevance of the content, topics were driven by community concerns and questions, as ascertained through research. Ana’s repository of educational content was based on national and international organizations as well as interdisciplinary experts. In the context of this development and pilot project, we identified an evaluation framework to explore reach, engagement, and satisfaction. RESULTS: A total of 626 community members used Ana from August 2021 to March 2022. Among those participants, 346 used the English version, with an average of 43 users per month; and 280 participants used the Spanish version, with an average of 40 users monthly. Across all users, 63.87% (n=221) of English users and 22.14% (n=62) of Spanish users returned to use Ana at least once; 18.49% (n=64) among the English version users and 18.57% (n=52) among the Spanish version users reported their ranking. Positive ranking comprised the “smiley” and “loved” emojis, and negative ranking comprised the “neutral,” “sad,” and “mad” emojis. When comparing negative and positive experiences, the latter was higher across Ana’s platforms (English: n=41, 64.06%; Spanish: n=41, 77.35%) versus the former (English: n=23, 35.93%; Spanish: n=12, 22.64%). CONCLUSIONS: This pilot project demonstrated the feasibility and capacity of an innovative ICT to share COVID-19 information within diverse communities. Creating a chatbot like Ana with bilingual content contributed to an equitable approach to address the lack of accessible COVID-19–related information. |
format | Online Article Text |
id | pubmed-9879317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-98793172023-01-27 COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots Perez-Ramos, Jose G Leon-Thomas, Mariela Smith, Sabrina L Silverman, Laura Perez-Torres, Claudia Hall, Wyatte C Iadarola, Suzannah JMIR Form Res Original Paper BACKGROUND: Disparities in COVID-19 information and vaccine access have emerged during the pandemic. Individuals from historically excluded communities (eg, Black and Latin American) experience disproportionately negative health outcomes related to COVID-19. Community gaps in COVID-19 education, social, and health care services (including vaccines) should be prioritized as a critical effort to end the pandemic. Misinformation created by the politicization of COVID-19 and related public health measures has magnified the pandemic’s challenges, including access to health care, vaccination and testing efforts, as well as personal protective equipment. Information and Communication Technology (ICT) has been demonstrated to reduce the gaps of marginalization in education and access among communities. Chatbots are an increasingly present example of ICTs, particularly in health care and in relation to the COVID-19 pandemic. OBJECTIVE: This project aimed to (1) follow an inclusive and theoretically driven design process to develop and test a COVID-19 information ICT bilingual (English and Spanish) chatbot tool named “Ana” and (2) characterize and evaluate user experiences of these innovative technologies. METHODS: Ana was developed following a multitheoretical framework, and the project team was comprised of public health experts, behavioral scientists, community members, and medical team. A total of 7 iterations of ß chatbots were tested, and a total of 22 ß testers participated in this process. Content was curated primarily to provide users with factual answers to common questions about COVID-19. To ensure relevance of the content, topics were driven by community concerns and questions, as ascertained through research. Ana’s repository of educational content was based on national and international organizations as well as interdisciplinary experts. In the context of this development and pilot project, we identified an evaluation framework to explore reach, engagement, and satisfaction. RESULTS: A total of 626 community members used Ana from August 2021 to March 2022. Among those participants, 346 used the English version, with an average of 43 users per month; and 280 participants used the Spanish version, with an average of 40 users monthly. Across all users, 63.87% (n=221) of English users and 22.14% (n=62) of Spanish users returned to use Ana at least once; 18.49% (n=64) among the English version users and 18.57% (n=52) among the Spanish version users reported their ranking. Positive ranking comprised the “smiley” and “loved” emojis, and negative ranking comprised the “neutral,” “sad,” and “mad” emojis. When comparing negative and positive experiences, the latter was higher across Ana’s platforms (English: n=41, 64.06%; Spanish: n=41, 77.35%) versus the former (English: n=23, 35.93%; Spanish: n=12, 22.64%). CONCLUSIONS: This pilot project demonstrated the feasibility and capacity of an innovative ICT to share COVID-19 information within diverse communities. Creating a chatbot like Ana with bilingual content contributed to an equitable approach to address the lack of accessible COVID-19–related information. JMIR Publications 2023-01-25 /pmc/articles/PMC9879317/ /pubmed/36630649 http://dx.doi.org/10.2196/39045 Text en ©Jose G Perez-Ramos, Mariela Leon-Thomas, Sabrina L Smith, Laura Silverman, Claudia Perez-Torres, Wyatte C Hall, Suzannah Iadarola. Originally published in JMIR Formative Research (https://formative.jmir.org), 25.01.2023. 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 use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Perez-Ramos, Jose G Leon-Thomas, Mariela Smith, Sabrina L Silverman, Laura Perez-Torres, Claudia Hall, Wyatte C Iadarola, Suzannah COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots |
title | COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots |
title_full | COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots |
title_fullStr | COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots |
title_full_unstemmed | COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots |
title_short | COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots |
title_sort | covid-19 vaccine equity and access: case study for health care chatbots |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879317/ https://www.ncbi.nlm.nih.gov/pubmed/36630649 http://dx.doi.org/10.2196/39045 |
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