ArticlePublic health action2022
Using digital chatbots to close gaps in healthcare access during the COVID-19 pandemic.
Article in Public health action, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 23 citations in OpenAlex.
- Engagement with a Chatbot-based Intervention for the delivery of mailed at-home COVID-19 Testing: a Descriptive Log Analysis Study from the SCALE-UP II Trial.medRxiv : the preprint server for health sciences · 2026Article
- Building EVA (Journal of medical Internet research · 2026Article
- Understanding capability, opportunity, and motivation for at-home COVID-19 testing in underserved populations during the pandemic.Translational behavioral medicine · 2026Article
- Future Me, a Prospection-Based Chatbot to Promote Mental Well-Being in Youth: Two Exploratory User Experience Studies.JMIR formative research · 2025Article
- Medical accuracy of artificial intelligence chatbots in oncology: a scoping review.The oncologist · 2025Article
- Care for adolescents with drug-susceptible pulmonary tuberculosis in Lima, Peru: a qualitative assessment.BMJ open · 2025Article
- Development and Refinement of a Chatbot for Birthing Individuals and Newborn Caregivers: Mixed Methods Study.JMIR pediatrics and parenting · 2024Article
- Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review.Journal of medical Internet research · 2024Review
- Development and Pilot-Testing of an Optimized Conversational Agent or "Chatbot" for Peruvian Adolescents Living With HIV to Facilitate Mental Health Screening, Education, Self-Help, and Linkage to Care: Protocol for a Mixed Methods, Community-Engaged Study.JMIR research protocols · 2024Article
- Evaluating the reliability of ChatGPT as a tool for imaging test referral: a comparative study with a clinical decision support system.European radiology · 2024Article
- One-day psychosocial education program for people with epilepsy: A continuation study of the "Epi-school".Heliyon · 2023Article
- Interventions to increase help-seeking for mental health care in low- and middle-income countries: A systematic review.PLOS global public health · 2023Article
- Article
- Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study.Turkish journal of emergency medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionChatbots have emerged as a first link to care in recent years. The COVID-19 pandemic, and consequent health system disruptions, expanded their use. Socios En Salud (SES) introduced chatbots in Peru, which experienced one of the highest excess COVID mortalities in the world.
methodsSES and the government identified unmet population health needs, which could be amenable to virtual interventions. Chatbots were developed to screen individuals for these conditions; we describe the period of deployment, number of screenings, and number of people who received services.
resultsBetween April 2020 and May 2021, SES deployed nine ChatBots: four for mental health, two for maternal and child health, and three for chronic diseases: breast cancer, hypertension, diabetes mellitus, and obesity. Mental health services were provided to 42,932 people, 99.99% of those offered services. The other ChatBots reached fewer people. Overall, more than 50% of eligible people accepted chatbot-based services. DISCUSSION: ChatBot use was highest for mental health. Chatbots may increase connections between a vulnerable population and health services; this is likely dependent on several factors, including condition, population, and penetration of smart phones. Future research will be critical to understand user experience and preferences and to ensure that chatbots link vulnerable populations to appropriate, high-quality care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.