Mobile based chatbot application for HIV/AIDS counseling using artificial intelligence markup language approach

Ardiana, Dewa Putu Yudhi; Joni, I Dewa Made Adi Baskara; Udayana, I. P. A. E. D.

doi:10.1088/1742-6596/1469/1/012041

Cited by 7 publications

(5 citation statements)

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“…Second, limited data were available to explore the barriers and facilitators of the application of Chatbot in PrEP care. As one of the promising compliments and alternatives in healthcare, we must carefully explore Chatbot-related topics in-depth in future studies [ 27 , 29 , 35 ]. Third, due to the scope of the current study, we did not assess how community-level socioeconomic characteristics (e.g., social vulnerability) impacted the applications of telemedicine in PrEP implementation in primary care settings among different groups [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

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Telemedicine Experience for PrEP Care among PrEP-Eligible Women and Their Primary Care Providers during the First Year of the COVID-19 Pandemic in the United States

et al. 2022

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(1) Background: During the two-year-long siege from the COVID-19 pandemic, a significant proportion of doctor visits transitioned from in-person to virtual. Scare evidence is available to assess the quality of patient-provider communication via the platform of telemedicine, especially for PrEP care within primary care settings. (2) Methods: Participants included 18 primary care providers and 29 PrEP-eligible women. Through content analysis and thematic analysis, facilitators and barriers embedded at different levels of telemedicine were identified and assessed. (3) Results: Women and providers reported pros and cons regarding their telemedicine experiences during the initial wave of COVID-19. Both groups of participants agreed that telemedicine visits were more convenient, efficient, and comfortable than in-person visits. However, without face-to-face interactions, some women felt less empathy, caring, and connected with their providers during virtual visits. Health providers expressed concerns with telemedicine, including patients’ privacy, lack of intimacy between patients and providers, and delayed lab work. (4) Conclusions: Our data indicate multi-level factors may affect telemedicine experience among PrEP-eligible women and health providers. Participants expressed concerns that may further entrench these long-existing health disparities in healthcare. Proactive efforts from policymakers, health professionals, researchers, and stakeholders are urgently required to tackle identified barriers and to pave the way for the new infrastructure that ensures health equity in society.

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Section: Discussionmentioning

confidence: 99%

“…To date, numerous research studies have thoroughly examined the role of telemedicine in healthcare, ranging from mental health and cancer treatment to lifestyle management [ 27 , 28 , 29 ]. However, few studies have examined the impact of telemedicine in HIV care including PrEP care during the COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 99%

Telemedicine Experience for PrEP Care among PrEP-Eligible Women and Their Primary Care Providers during the First Year of the COVID-19 Pandemic in the United States

et al. 2022

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“…Penelitian [5] mengembangkan aplikasi chatbot kampus menggunakan Artificial Intelligence Markup Language dalam bahasa myanmar. Penelitian [14] mengembangkan aplikasi chabot berbasis mobile yang digunakan untuk konseling HIV/AIDS menggunakan metode AIML. Penelitian [15] mengembangkan chatbot yang digunakan untuk asisten e-commerce dengan metode AIML.…”

Section: Pendahuluanunclassified

Aplikasi Chatbot untuk Layanan Informasi dan Akademik Kampus Berbasis Artificial Intelligence Markup Language (AIML)

2020

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Saat ini sebagian besar universitas menggunakan sistem informasi web untuk menyampaikan informasi terkait informasi pendaftaran mahasiswa, akademik, beasiswa, biaya pendidikan dan lain-lain. Dalam hal pelayanan pendidikan, tentunya universitas perlu memberikan layanan yang terbaik, agar para civitas akademik kampus, masyarakat mendapatkan kepuasan terhadap layanan yang diberikan. Penelitian ini adalah mengembangkan aplikasi chatbot yang dapat digunakan sebagai layanan informasi kampus dan akademik bagi masyarakat umum maupun bagi civitas akademik kampus Universitas Lancang Kuning Adapun tahapan dalam pengembangan aplikasi chatbot ini diantaranya adalah pengumpulan kebutuhan, desain, membuat prototype, evaluasi dan perbaikan. Adapun metode yang digunakan untuk pembelajaran chatbot menggunakan Artificial Markup Language (AIML). Knowledge dari aplikasi chatbot ini adalah alamat kampus, syarat pendaftaran, langkah pendaftaran, program studi, jalur kuliah, berapa biasa kuliah dan cara daftar. Berdasarkan hasil pengujian yang telah dilakukan dengan metode whitebox dan blackbox, bahwa aplikasi chatbot dapat berjalan dengan baik sebesar 100%. Sedangkan pengujian menggunakan UAT sebesar 95%. Hal tersebut menunjukkan bahwa aplikasi chatbot mampu menjawab pertanyaan-pertanyaan yang diajukan, sesuai dengan pengetahuan yang telah diberikan sebelumnya. . Kata kunci: Akademik, AIML, Chatbot. Abstract Currently, most universities use web-based information systems to provide information on student enrollment, academics, scholarships, fees, and so on. As far as education services are concerned, of course, universities need to offer the best service to ensure that the campus learning community is happy with the services offered. The goal of this research is to build a chatbot application that can use as a campus and academic information service for the general public and the academic community of Universitas Lancang Kuning. The phases in the development of this chatbot framework include gathering needs, designing, creating prototypes, testing and improving. The tool used to learn the chatbot uses the Artificial Markup Language (AIML). Knowledge from the chatbot application is the campus address, registration requirements, registration steps, study program, course path, how many lectures are used and how to register. Based on the results of the tests carried out using the Whitebox and BlackBox methods, the chatbot framework will run 100% . While testing using UAT is 95%. The Chatbot framework can answer the questions that have been posed, based on the information that has been previously given. Keywords: Academic, AIML, Chatbot

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“…In the field of HIV, positive experiences have been reported, with chatbots providing flexibility and agility in contacting health teams and institutions [ 16 ], as well as introducing counseling to young people quickly and reliably [ 17 ]. By contrast, chatbots sending reminders for medication use and scheduling consultation can be perceived as invasive and less accepted [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Transgender Chatbot (Amanda Selfie) to Create Pre-exposure Prophylaxis Demand Among Adolescents in Brazil: Assessment of Acceptability, Functionality, Usability, and Results

Massa¹,

Grangeiro²,

Ferraz³

et al. 2023

J Med Internet Res

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Background HIV incidence rates have increased in adolescent men who have sex with men (AMSM) and adolescent transgender women (ATGW). Thus, it is essential to promote access to HIV prevention, including pre-exposure prophylaxis (PrEP), among these groups. Moreover, using artificial intelligence and online social platforms to create demand and access to health care services are essential tools for adolescents and youth. Objective This study aims to describe the participative process of developing a chatbot using artificial intelligence to create demand for PrEP use among AMSM and ATGW in Brazil. Furthermore, it analyzes the chatbot’s acceptability, functionality, and usability and its results on the demand creation for PrEP. Methods The chatbot Amanda Selfie integrates the demand creation strategies based on social networks (DCSSNs) of the PrEP1519 study. She was conceived as a Black transgender woman and to function as a virtual peer educator. The development process occurred in 3 phases (conception, trial, and final version) and lasted 21 months. A mixed methodology was used for the evaluations. Qualitative approaches, such as in-depth adolescent interviews, were used to analyze acceptability and usability, while quantitative methods were used to analyze the functionality and result of the demand creation for PrEP based on interactions with Amanda and information from health care services about using PrEP. To evaluate Amanda’s result on the demand creation for PrEP, we analyzed sociodemographic profiles of adolescents who interacted at least once with her and developed a cascade model containing the number of people at various stages between the first interaction and initiation of PrEP (PrEP uptake). These indicators were compared with other DCSs developed in the PrEP1519 study using chi-square tests and residual analysis (P=.05). Results Amanda Selfie was well accepted as a peer educator, clearly and objectively communicating on topics such as gender identity, sexual experiences, HIV, and PrEP. The chatbot proved appropriate for answering questions in an agile and confidential manner, using the language used by AMSM and ATGW and with a greater sense of security and less judgment. The interactions with Amanda Selfie combined with a health professional were well evaluated and improved the appointment scheduling. The chatbot interacted with most people (757/1239, 61.1%) reached by the DCSSNs. However, when compared with the other DCSSNs, Amanda was not efficient in identifying AMSM/ATGW (359/482, 74.5% vs 130/757, 17.2% of total interactions, respectively) and in PrEP uptake (90/359, 25.1% vs 19/130, 14.6%). The following profiles were associated (P<.001) with Amanda Selfie’s demand creation, when compared with other DCS: ATGW and adolescents with higher levels of schooling and White skin color. Conclusions Using a chatbot to create PrEP demand among AMSM and ATGW was well accepted, especially for ATGW with higher levels of schooling. A complimentary dialog with a health professional increased PrEP uptake, although it remained lower than the results of the other DCSSNs.

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Mobile based chatbot application for HIV/AIDS counseling using artificial intelligence markup language approach

Cited by 7 publications

References 1 publication

Telemedicine Experience for PrEP Care among PrEP-Eligible Women and Their Primary Care Providers during the First Year of the COVID-19 Pandemic in the United States

Telemedicine Experience for PrEP Care among PrEP-Eligible Women and Their Primary Care Providers during the First Year of the COVID-19 Pandemic in the United States

Aplikasi Chatbot untuk Layanan Informasi dan Akademik Kampus Berbasis Artificial Intelligence Markup Language (AIML)

A Transgender Chatbot (Amanda Selfie) to Create Pre-exposure Prophylaxis Demand Among Adolescents in Brazil: Assessment of Acceptability, Functionality, Usability, and Results

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