ArticleInternational journal of medical informatics2024
Development and feasibility testing of an artificially intelligent chatbot to answer immunization-related queries of caregivers in Pakistan: A mixed-methods study.
Article in International journal of medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of stage-of-change (SOC)-tailored interventions in increasing uptake of any type of vaccination: A systematic review and meta-analysis.Applied psychology. Health and well-being · 2025Pooled it
- A Hybrid Chatbot to Promote Pneumococcal Vaccination Among Older Adults: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Stakeholder Experiences With the Pneumococcal Conjugate Vaccine Chatbot as a Complementary Capacity-Building Tool for Frontline Health Workers in India: Qualitative Study.JMIR formative research · 2026Article
- The development and use of chatbots in enhancing health care access for underserved and vulnerable populations: a scoping review.BMC public health · 2025Article
- Applications of Artificial Intelligence in the Control of Infectious Diseases in the Post-COVID Era: Scoping Review.JMIR nursing · 2025Article
- Current Status of Information and Communication Technologies Utilization, Education Needs, Mobile Health Literacy, and Self-Care Education Needs of a Population of Stroke Patients.Healthcare (Basel, Switzerland) · 2025Article
- Improving training on hepatitis B research in Nigeria: Findings from an innovation bootcamp to strengthen capacity.PLOS global public health · 2025Article
- Exploring evaluation measures of large language models for family caregiver use: A scoping review.Digital healthReview
- Harnessing artificial intelligence and digital technology for enhancing routine immunization among zero-dose children.Digital healthReview
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGaps in information access impede immunization uptake, especially in low-resource settings where cutting-edge and innovative digital interventions are limited given the digital inequity. Our objective was to develop an Artificially Intelligent (AI) chatbot to respond to caregiver's immunization-related queries in Pakistan and investigate its feasibility and acceptability in a low-resource, low-literacy setting.
methodsWe developed Bablibot (Babybot), a local language immunization chatbot, using Natural Language Processing (NLP) and Machine Learning (ML) technologies with Human in the Loop feature. We evaluated the bot through a sequential mixed-methods study. We enrolled caregivers visiting the 12 selected immunization centers for routine childhood vaccines. Additional caregivers were reached through targeted text message communication. We assessed Bablibot's feasibility and acceptability by tracking user engagement and technological metrics, and through thematic analysis of in-depth interviews with 20 caregivers.
findingsBetween March 9, 2020, and April 15, 2021, 2,202 caregivers were enrolled in the study, of which, 677 (30.7%) interacted with Bablibot (users). Bablibot responded to 1,877 messages through 874 conversations. Conversation topics included vaccination due dates (32.4%; 283/874), side-effect management (15.7%;137/874), or delaying vaccination due to child's illness or COVID-lockdown (16.8%;147/874). Over 90% (277/307) of responses to text-based exit surveys indicated satisfaction with Bablibot. Qualitative analysis showed caregivers appreciated Bablibot's usefulness and provided feedback for further improvement of the system.
conclusionOur results demonstrate the feasibility and acceptability of local-language NLP chatbots in providing real-time immunization information in low-resource settings. Text-based chatbots canminimize the workload on helpline operators, in addition to instantaneously resolving caregiver queries that otherwise lead to delay or default.
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