Evidence map›Paper›PMID 39042446›Full record

ReviewJournal of medical Internet research2024

Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review.

Moustafa Laymouna, Yuanchao Ma, David Lessard, Tibor Schuster, Kim Engler, Bertrand Lebouché

Abstract readReview
In one paragraph

Review in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
122citing papers in PubMed, 4 pooled it
–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

122 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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62 more citing papers are in PubMed but not listed here.

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

6 authors.

Moustafa LaymounaDepartment of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.ORCID 0000-0002-0673-6356
Yuanchao MaCentre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.ORCID 0000-0002-4048-1705
David LessardCentre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.ORCID 0000-0002-1151-3763
Tibor SchusterDepartment of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.ORCID 0000-0002-3620-3526
Kim EnglerCentre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.ORCID 0000-0001-8364-7421
Bertrand LebouchéDepartment of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.ORCID 0000-0002-1273-9393

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field.

objectiveThis review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations.

methodsA rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis.

resultsThe review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts.

conclusionsHealth care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.

Indexed as

Delivery of Health CareCommunicationHumansTelemedicineAIartificial intelligencechatbotconversational agentconversational assistantdigital healthelectronic healthhealth information technologymobile healthtelehealthuser-computer interface

Identifiers

PMID39042446
PMCPMC11303905

What Socratic 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 Socratic graph.