Evidence map›Paper›PMID 40171527›Full record

ArticleFrontiers in digital health2025

Experience developing a pediatric medical chatbot in Singapore: a digital innovation for improved emergency care.

Choo Min, Rong Xiu Cynthia Lim, Sek Wan Tan, Sashikumar Ganapathy

Abstract read
In one paragraph

Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Choo MinDepartment of Emergency Medicine, KK Women's and Children's Hospital, Singapore, Singapore.
Rong Xiu Cynthia LimDepartment of Emergency Medicine, KK Women's and Children's Hospital, Singapore, Singapore.
Sek Wan TanDepartment of Emergency Medicine, KK Women's and Children's Hospital, Singapore, Singapore.
Sashikumar GanapathyDepartment of Emergency Medicine, KK Women's and Children's Hospital, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This community case study explores the lessons learnt from the development of the Urgent Paediatric Advice Line (UPAL), a medical chatbot designed to address key challenges in pediatric healthcare, including emergency department (ED) overcrowding, health-seeking behavior, and health literacy. The chatbot was developed by pediatric specialists in collaboration with an AI-driven technology partner to provide caregivers with timely, accurate, and accessible guidance for managing pediatric health concerns. By helping parents assess the severity of their child's symptoms and navigate appropriate care pathways, UPAL aims to reduce unnecessary ED visits and improve health literacy. The development process employed an iterative, user-centered approach to refine the algorithm and enhance the user experience, with key challenges including balancing clinical reliability with user empathy. By offering evidence-based advice tailored to individual symptoms, UPAL empowers caregivers to make more informed decisions about their child's care. This case study highlights the potential of digital health solutions to empower caregivers, improve patient engagement, and increase healthcare access, particularly in pediatric settings. The study underscores the lessons for the field-namely the importance of interdisciplinary collaboration, continuous iterative development, patient-centered design, and active stakeholder engagement in creating effective digital health tools. Looking forward, future developments will include the incorporation of generative AI to provide more humanistic and personalized responses, as well as the creation of a post-discharge outreach module to provide proactive post-discharge support to caregivers, further enhancing healthcare delivery in a rapidly evolving digital landscape.

Indexed as

caregiver empowermentdigital healthmedical chatbotpediatric acute carepre-hospital triage

Identifiers

PMID40171527
PMCPMC11959085

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.