Evidence map›Paper›PMID 36630649›Full record

ArticleJMIR formative research2023

COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots.

Jose G Perez-Ramos, Mariela Leon-Thomas, Sabrina L Smith, Laura Silverman, Claudia Perez-Torres, Wyatte C Hall, Suzannah Iadarola

Abstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Chatbots and COVID-19: Taking Stock of the Lessons Learned.Journal of medical Internet research · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jose G Perez-RamosDepartment of Public Health Sciences, School of Medicine and Dentistry, University of Rochester, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-6654-6984
Mariela Leon-ThomasDepartment of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-8597-1118
Sabrina L SmithDepartment of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-4215-2112
Laura SilvermanDepartment of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States.ORCID https://orcid.org/0000-0003-2934-9831
Claudia Perez-TorresDepartment of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-3669-9277
Wyatte C HallDepartment of Public Health Sciences, School of Medicine and Dentistry, University of Rochester, Rochester, NY, United States.ORCID https://orcid.org/0000-0002-7974-973X
Suzannah IadarolaDepartment of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States.ORCID https://orcid.org/0000-0001-6828-8379

Funding

The University of Rochester's Clinical and Translational Science InstituteUL1TR002001 · NCATS · UNIVERSITY OF ROCHESTER · PI WILSON, KAREN M., ZAND, MARTIN S · 2016 to 2024
$34.6M
NCATS NIH HHS UL1 TR002001
6 · The paper itself

Abstract

backgroundDisparities 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.

objectiveThis 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.

methodsAna 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.

resultsA 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%).

conclusionsThis 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.

Indexed as

chatbotchatbot developmentchatbot toolcommunityCOVID-19health care gaphealth disparityhealth equityhealth informationhealth outcomeICTInformation and Communication TechnologymHealthminority populationmobile healthuser experience

Identifiers

PMID36630649
PMCPMC9879317

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.