CC BY
29
Российский медико-биологический вестник
ПИСЬМА В РЕДАКЦИЮ Том 30, № 4, 2022 имени академика И. П. Павлова
УДК 616.988-084:614.47
DOI: https://doi.org/10.17816/PAVL0VJ91196
Концептуальная основа разработки чат-ботов для борьбы с проблемой нерешительности в отношении COVID-19-вакцины
D. R. Neog1, Rh . Grover1, R . Ransing2 R . Ramalho3
1 Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, Assam, India;
2 BKL Walwalkar Rural Medical College, Sawarde, Ratnagiri, Maharashtra, India;
3 University of Auckland, Auckland, New Zealand
АННОТАЦИЯ
Цель данного письма — обсудить возможности чат-ботов, предназначенных для ответов на вопросы лиц, сомневающихся в вакцинации, их пользу и ограничения в устранении психосоциальных детерминант нерешительности в отношении вакцины против COVID-19 . Авторы предлагают концептуальную основу разработки чат-ботов на основе искусственного интеллекта и обобщают основные характеристики обсуждаемых сегодня в литературе четырех чат-ботов, разработанных для решения проблемы нерешительности в отношении вакцины против COVID-19 .
Ключевые слова: COVID-19; нерешительность в отношении вакцины; чат-боты; медицинские работники
Для цитирования:
Neog D.R., Grover Rh., Ransing R., Ramalho R. Концептуальная основа разработки чат-ботов для борьбы с проблемой нерешительности в отношении COVID-19-вакцины // Российский медико-биологический вестник имени академика И.П. Павлова. 2022. Т. 30, № 4. С. 575-580. D0I: https://doi.org/10.17816/PAVL0VJ91196
Рукопись получена: 05 . 08 . 2022 Рукопись одобрена: 14 . 09 . 2022 Опубликована: 31.12 . 2022
„ "Г^, т „ ■ Лицензия СС ВУ-МС-Ш 40
© Коллектив авторов, 2022
LETTERS IN EDITION 576 -
Vol. 30 (4) 2022
DOI: https://doi.org/10.17816/PAVL0VJ91196
A Conceptual Framework for the Development of Chatbots Addressing COVID-19 Vaccine Hesitancy
Debanga R . Neog1' Rhythm Grover1, Ramdas Ransing2 Rodrigo Ramalho3
1 Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, Assam, India;
2 BKL Walwalkar Rural Medical College, Sawarde, Ratnagiri, Maharashtra, India;
3 University of Auckland, Auckland, New Zealand
ABSTRACT
Aim of this letter is to review Chatbots that answer questions of vaccine-hesitant people, their utility, and their limitations in addressing psycho-social determinants of COVID-19 vaccine hesitancy . The authors propose a conceptual framework that could guide the development of artificial intelligence-based Chatbots and summarise the salient features of the four Chatbots discussed in the literature today, developed to address COVID-19 vaccine hesitancy .
Keywords: COVID-19; vaccine hesitancy; chatbots; healthcare workers
For citation:
Neog DR, Grover Rh, Ransing R, Ramalho R. A Conceptual Framework for the Development of Chatbots Addressing COVID-19 Vaccine Hesitancy. I.P. Pavlov Russian Medical Biological Herald. 2022;30(4):575-580. DOI: https://doi.org/10.17816/PAVL0VJ91196
Received: 05 . 08 . 2022 Accepted: 14 . 09 . 2022 Published: 31.12 . 2022
E c 0 • V E c T 0 R The article can be use under the CC BY-NC-ND 40 license
© Authors, 2022
Российский медико-биологический вестник
ПИСЬМА В РЕДАКЦИЮ Том 30, № 4, 2022 имени академика И. П. Павлова - 577
LIST OF ABBREVIATIONS
AI-based — Artificial Intelligence-based COVID-19 — COronaVIrus Disease 2019 CVH — COVID-19 vaccine hesitancy FAQs — frequently asked questions HCWs — healthcare workers MET — motivational enhancement therapy
Vaccination is one of the most effective and low-cost public health interventions against at least 20 vaccine-preventable diseases, including COronaVIrus Disease 2019 (COVID-19) [1] . However, vaccine hesitancy is a growing public concern affecting vaccination coverage worldwide . Many countries have deployed several psychosocial strategies to address the determinants of vaccine hesitancy. Examples include creating awareness on social media, government websites, and media through frequently asked questions (FAQs) . In addition, some countries have developed Chatbots to answer questions vaccine-hesitant people might have
Table 1 summarises the salient features of the four Chatbots discussed in the literature today, developed to address COVID-19 vaccine hesitancy (CVH) . In this letter, we aimed to review these Chatbots, their utility, and their limitations in addressing psycho-social determinants of CVH Also, we propose a conceptual framework that could guide
the development of artificial intelligence-based Chatbots addressing these determinants
These Chatbots have the potential to reduce the burden on healthcare workers (HCWs) by providing support and relevant information to them and patients The Chatbots may assist primary HCWs in organizing specific information related to the COVID-19 vaccines . Furthermore, Chatbots-based approachesare more adaptable, trustworthy, and scalable in low-resource settings However, these Chatbots may be ineffective at assessing and addressing the determinants of CVH at an individual level [2] The main reason for this potential ineffectiveness is a lack of emphasis on individual concerns/ underlying motives and specific determinants (e . g ., confidence, collective responsibility) of CVH Also, there is insufficient evidence related to their overall effectiveness and utility Finally, there is the lack of involvement of primary HCWs, despite the well-known fact that primary HCWs are reputed to be the most reliable source of vaccine-related informations [3] .
Table 1. Brief review of Chatbots developed to address COVID-19 vaccine hesitancy across the world
MyGOV Corona Helpdesk [7] The government of India and Haptik Infotech Pvt. Ltd. developed this Chatbot to provide timely updates and help citizens clear their queries on COVID-19 infection and COVID-19 vaccines. It is deployed on WhatsApp and is used by Indian users. It clarifies false information and answers frequently asked questions related to COVID-19 vaccines.
Corowa-Kun [8] Developed and tested in Japan and found effective in increasing the vaccine acceptance rate from 59% to 80%. Corowa-Kun has been deployed in Japan's most popular messaging app, 'LINE', which almost two-thirds of its population use. The Chatbot helps to understand the COVID-19 vaccines.
Vira (Vaccine Information Resource Assistant) [9] Researchers of the John Hopkins Bloomberg School of Public Health, along with IBM, developed an AI-powered Chatbot in October 2021 to combat vaccine hesitancy in young Americans. The Chatbot is specifically designed to provide credible information about COVID-19 vaccines via WhatsApp and the Web. It will continue to evolve as more people use it and provide relevant feedback.
French and Twilio chatbot [10] Addresses concerns about the COVID-19 vaccines by providing the information to the general population.
To address the above mentioned issues, we propose a conceptual framework for developing Artificial intelligence (machine learning)-based intelligent Chatbots for primary HCWs (Figure 1) . Our proposed framework includes the information-deficit model of communication [4], the 5 C model of vaccine hesitancy [5], motivational interviewing [6], and involvement of primary HCWs .
The main input for this Chatbot will be standardized scales or questions (in the form of text) related to CVH and motivational interviewing . The answers to these questions,
Motivational Interviewing staging, and decision support (in the form of text) will be output The framework includes screening and intervention for CVH Using mathematical or statistical models (e g , multiple regression, logistic regression, cluster analysis, principal component analysis, and discriminant analysis) and machine learning algorithms (e g , deep neural network-based embedding generation and sequencing modeling for natural language processing), the individual or combination of factors that predict CVH can be identified The tailor-made strategies can then be
LETTERS IN EDITION
Vol. 30 (4) 2022
Fig. 1. tanceptuai Framework for Chartbot (machine learning-based) for COVID-19 vaccine hesitancy
Notes: Determinants of Vaccine hesitance (A): A1 — confidence, A2 — complacency, A3 — constraints, A4 — calculation, A5 — collective responsibility; Motivational staging (B): B1 — pre-contemplation, B2 — contemplation, B3 — preparation/decision, B4 — action; fr Hypothetical factor/construct for which answers and strategies we be sought or developed using a living database; COVID-19 — COronaVIrus Disease 2019, CVH — COVID-19 vaccine hesitancy, MET — motivational enhancement therapy.
developed using the living database comprising answers to questions and scientific information . The determinants of CVH are dynamic, rapidly evolving, and interconnected . Also, there is no umbrella concept that embraces all determinants at the time of development As a result, the database should be a living one that can be constantly edited and updated to address the emerging antecedents
Artificial Intelligence-based Chatbots developed using our conceptual framework will be evidence-based, more robust, and with more efficient and accurate algorithms that can deal with data with complex distributions to quickly generate more relevant and empathetic responses . Previous studies reported huge variation in CVH, attributed to the use of different questionnaires and cut-off scores to categorize vaccine hesitancy among the population These factors are frequently complex, dynamic, unpredictable, and challenging to address The Artificial Intelligence-based Chatbots using standardized scales will reduce these variations and may help monitor rapidly changing factors . After minimal training of HCWs, the Chatbots will assist them in providing an individual-level intervention to address the determinants of CVH The Chatbots can be deployed in various health care settings such as hospitals, clinics, and communities In addition, these Chatbots can be integrated into existing national immunization programs, maternal and child health services, and vaccination campaigns At the same time, the Chatbot-based telephonic reminders included in the model may further improve the vaccine uptake rate
As Chatbots can be easily installed into smartphones and integrated with websites, they could be more feasible,
acceptable, scalable, cost-effective, sustainable, and useful in the current COVID-19 pandemic . These Chatbots primarily focus on primary HCWs, who play an important role in the implementation of the vaccination drive and are often the most trusted source of vaccine-related information However, primary HCWs may have difficulty organizing screening tools, identifying the specific components of the intervention, and assessing participant motivation . Also, the information about the COVID-19 vaccines can be too complex to recall correctly during intervention delivery. So, despite their training, the information they provide on these vaccines may be inconsistent . Most of these issues can be addressed by developing artificial intelligence (Al)-based Chatbots using our conceptual framework. The Al-based approach will help in automatic data collection, accurate assessment, and integration with application programming interfaces and, thus, will promote the systematic identification of factors that contribute to CVH . Future studies are needed to evaluate the effectiveness of Al-based Chatbots using controlled trials or quasi-experimental, interrupted time series design . These studies will also provide an opportunity to develop effective and efficient evidence-based strategies
ADDITIONALLY
Funding. Ransing Ramdas has received a research grant from the Indian Council of Medical Research for the COVID-19 Vaccine Confidence Project for Pregnant and Lactating Women (CTRI/2022/02/040359). Conflict of interests. The authors declare no conflicts of interests. Финансирование. Ransing Ramdas получил исследовательский грант от Индийского совета медицинских исследований для проекта
ПИСЬМА В РЕДАКЦИЮ
Том 30, № 4, 2022
Российский медико-биологический вестник имени академика И. П. Павлова
«Уверенность в вакцинации против СОУЮ-19 для беременных и кормя- Конфликт интересов. Авторы заявляют об отсутствии конфликта щих женщин» (СТР!/2022/02/040359). интересов.
СПИСОК ИСТОЧНИКОВ
1. WHO. Vaccine-preventable Diseases [Internet]. 2022. Доступно по: https://www.who.int/teams/regulation-prequalification/eul/immunization-vaccines-and-biologicals. Ссылка активна на 05.08.2022.
2. Ransing R., Kukreti P., Raghuveer P., et al. A brief psycho-social intervention for COVID-19 vaccine hesitancy among perinatal women in low-and middle-income countries: Need of the hour // Asian Journal of Psychiatry. 2022. Vol. 67. P. 102929. doi: 10.1016/j.ajp.2021.102929
3. Solis Arce J.S., Warren S.S., Meriggi N.F., et al. COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries // Nature Medicine. 2021. Vol. 27, № 8. P. 1385-1394. doi: 10.1038/s41591-021-01454-y
4. Lehner L., Gribi J., Hoffmann K., et al. Beyond the "information deficit model" — understanding vaccine-hesitant attitudes of midwives in Austria: a qualitative study // BMC Public Health. 2021. Vol. 21, № 1. P. 1671. doi: 10.1186/s12889-021-1 1710-y
5. Betsch C., Schmid P., Heinemeier D., et al. Beyond confidence: Development of a measure assessing the 5C psychological antecedents of vaccination // PLoS One. 2018. Vol. 13, № 12. P. e0208601. doi: 10.1371/journal.pone.0208601
6. Gagneur A. Motivational interviewing: A powerful tool to address vaccine hesitancy // Canada Communicable Disease Report. 2020. Vol. 46, № 4. P. 93-97. doi: 10.14745/ccdr.v46i04a06
7. WhatsApp chatbot MyGov Corona Helpdesk sees over 2 crore users [Internet]. 2022. Доступно по: https://digitalindia.gov.in/content/ whatsapp-chatbot-mygov-corona-helpdesk-sees-over-2-crore-users. Ссылка активна на 05.08.2022.
8. Kobayashi T., Nishina Y., Tomoi H., et al. 439. Corowa-kun: Impact of a COVID-19 Vaccine Information Chatbot on Vaccine Hesitancy, Japan 2021 // Open Forum Infectious Diseases. 2021 ;8(Suppl. 1):S321-S322. doi: 10.1093/ofid/ofab466.638
9. VIRA Vaccine Chatbot is now on WhatsApp [Internet]. 2022. Доступно по: https://www.jhsph.edu/ivac/2022/01/24/vira-whatsapp-launch/. Ссылка активна на 05.08.2022.
10. Altay S., Hacquin A.-S., Chevallier C., et al. Information Delivered by a Chatbot Has a Positive Impact on COVID-19 Vaccines Attitudes and Intentions // Journal of Experimental Psychology: Applied. 2021. Доступно по: https://doi.org/10.1037/xap0000400. Ссылка активна на 05.08.2022.
REFERENCES
1. WHO. Vaccine-preventable Diseases [Internet]. 2022. Available at: https://www.who.int/teams/regulation-prequalification/eul/immunization-vaccines-and-biologicals. Accessed: 2022 August 08.
2. Ransing R, Kukreti P, Raghuveer P, et al. A brief psycho-social intervention for COVID-19 vaccine hesitancy among perinatal women in low-and middle-income countries: Need of the hour. Asian Journal of Psychiatry. 2022;67:102929. doi: 10.1016/j.ajp.2021.102929
3. Solis Arce JS, Warren SS, Meriggi NF, et al. COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nature Medicine. 2021 ;27(8): 1385-94. doi: 10.1038/s41591-021-01454-y
4. Lehner L, Gribi J, Hoffmann K, et al. Beyond the "information deficit model" — understanding vaccine-hesitant attitudes of midwives in Austria: a qualitative study. BMC Public Health. 2021 ;21 (1 ): 1671. doi: 10.1186/s12889-021-1 1710-y
5. Betsch C, Schmid P, Heinemeier D, et al. Beyond confidence: Development of a measure assessing the 5C psychological antecedents of vaccination. PLoS One. 2018; 13( 12):e0208601. doi: 10.1371/journal. pone.0208601
6. Gagneur A. Motivational interviewing: A powerful tool to address vaccine hesitancy. Canada Communicable Disease Report. 2020;46(4):93-7. doi: 10.14745/ccdr.v46i04a06
7. WhatsApp chatbot MyGov Corona Helpdesk sees over 2 crore users [Internet]. 2022. Available at: https://digitalindia.gov.in/content/whatsapp-chatbot-mygov-corona-helpdesk-sees-over-2-crore-users. Accessed: 2022 August 08.
8. Kobayashi T, Nishina Y, Tomoi H, et al. 439. Corowa-kun: Impact of a COVID-19 Vaccine Information Chatbot on Vaccine Hesitancy, Japan 2021. Open Forum Infectious Diseases. 2021 ;8(Suppl 1 ):S321 -2. doi: 10.1093/ofid/ofab466.638
9. VIRA Vaccine Chatbot is now on WhatsApp [Internet]. 2022. Available at: https://www.jhsph.edu/ivac/2022/01/24/vira-whatsapp-launch/. Accessed: 2022 August 08.
10. Altay S, Hacquin A-S, Chevallier C, et al. Information Delivered by a Chatbot Has a Positive Impact on COVID-19 Vaccines Attitudes and Intentions. Journal of Experimental Psychology: Applied. 2021. Available at: https://doi.org/10.1037/xap0000400. Accessed: 2022 August 08.
ОБ АВТОРАХ
Neog Debanga Raj, B. Tech., PhD, Assistant Professor; ORCID: https://orcid.org/0000-0002-2794-4787; e-mail: dneog@iitg.ac.in
Grover Rhythm, MSc, PhD (Statistics), Assistant Professor; ORCID: https://orcid.org/0000-0002-0759-7554; e-mail: rhythmgrover@iitg.ac.in
AUTHOR'S INFO
Debanga R. Neog, B. Tech., PhD, Assistant Professor; ORCID: https://orcid.org/0000-0002-2794-4787; e-mail: dneog@iitg.ac.in
Rhythm Grover, Msc, PhD (Statistics), Assistant Professor; ORCID: https://orcid.org/0000-0002-0759-7554; e-mail: rhythmgrover@iitg.ac.in
LETTERS IN EDITION
Vol. 30 (4) 2022
*Ransing Ramdas, MD, Associate Professor; ORCID: https://orcid.org/0000-0002-5040-5570, e-mail: ramdas_ransing123@yahoo.co.in
Ramalho Rodrigo, MD, PhD, Senior Lecturer; ORCID: https://orcid.org/0000-0002-2372-6854; e-mail: r.ramalho@auckland.ac.nz
*Ramdas Ransing, MD, Associate Professor; ORCID: https://orcid.org/0000-0002-5040-5570, e-mail: ramdas_ransing123@yahoo.cojn
Rodrigo Ramalho, MD, PhD,Senior Lecturer; ORCID: https://orcid.org/0000-0002-2372-6854; e-mail: r.ramalho@auckland.ac.nz
* Автор, ответственный за переписку / Corresponding author
