Evidence map›Paper›PMID 37219234›Full record

ArticleJMIR formative research2023

Patient Acceptability of Symptom Screening and Patient Education Using a Chatbot for Autoimmune Inflammatory Diseases: Survey Study.

Tze Chin Tan, Nur Emillia Binte Roslan, James Weiquan Li, Xinying Zou, Xiangmei Chen, Ratnasari, Anindita Santosa

Open access · goldAbstract 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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
4.4field-weighted citation impact, top 6% of its field
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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Trial
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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 at 3 institutions in 1 country.

Tze Chin Tan *Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore.ORCID https://orcid.org/0000-0003-3859-6906
Nur Emillia Binte RoslanMedicine Academic Clinical Programme, SingHealth-Duke-NUS, Singapore, Singapore.ORCID https://orcid.org/0000-0003-0465-8734
James Weiquan LiMedicine Academic Clinical Programme, SingHealth-Duke-NUS, Singapore, Singapore.ORCID https://orcid.org/0000-0002-5241-4278
Xinying ZouInternal Medicine Clinic, Changi General Hospital, Singapore, Singapore.ORCID https://orcid.org/0009-0008-0658-7777
Xiangmei ChenInternal Medicine Clinic, Changi General Hospital, Singapore, Singapore.ORCID https://orcid.org/0009-0007-6032-7151
RatnasariInternal Medicine Clinic, Changi General Hospital, Singapore, Singapore.ORCID https://orcid.org/0009-0009-4944-5856
Anindita Santosa *Medicine Academic Clinical Programme, SingHealth-Duke-NUS, Singapore, Singapore.ORCID https://orcid.org/0000-0002-7320-4552
Changi General Hospital · SGSingapore General Hospital · SGSingHealth Duke-NUS Academic Medical Centre · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatbots have the potential to enhance health care interaction, satisfaction, and service delivery. However, data regarding their acceptance across diverse patient populations are limited. In-depth studies on the reception of chatbots by patients with chronic autoimmune inflammatory diseases are lacking, although such studies are vital for facilitating the effective integration of chatbots in rheumatology care.

objectiveWe aim to assess patient perceptions and acceptance of a chatbot designed for autoimmune inflammatory rheumatic diseases (AIIRDs).

methodsWe administered a comprehensive survey in an outpatient setting at a top-tier rheumatology referral center. The target cohort included patients who interacted with a chatbot explicitly tailored to facilitate diagnosis and obtain information on AIIRDs. Following the RE-AIM (Reach, Effectiveness, Adoption, Implementation and Maintenance) framework, the survey was designed to gauge the effectiveness, user acceptability, and implementation of the chatbot.

resultsBetween June and October 2022, we received survey responses from 200 patients, with an equal number of 100 initial consultations and 100 follow-up (FU) visits. The mean scores on a 5-point acceptability scale ranged from 4.01 (SD 0.63) to 4.41 (SD 0.54), indicating consistently high ratings across the different aspects of chatbot performance. Multivariate regression analysis indicated that having a FU visit was significantly associated with a greater willingness to reuse the chatbot for symptom determination (P=.01). Further, patients' comfort with chatbot diagnosis increased significantly after meeting physicians (P<.001). We observed no significant differences in chatbot acceptance according to sex, education level, or diagnosis category.

conclusionsThis study underscores that chatbots tailored to AIIRDs have a favorable reception. The inclination of FU patients to engage with the chatbot signifies the possible influence of past clinical encounters and physician affirmation on its use. Although further exploration is required to refine their integration, the prevalent positive perceptions suggest that chatbots have the potential to strengthen the bridge between patients and health care providers, thus enhancing the delivery of rheumatology care to various cohorts.

Indexed as

autoimmune rheumatic diseaseschatbotconversational agentsdigital health interventiondigital technology in medicineearly diagnosiseducationimplementationpatient‒physician interactionspatient surveyrheumatology

Identifiers

PMID37219234
PMCPMC11019963
OpenAlexW4377565164

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.