Evidence map›Paper›PMID 40873736›Full record

ArticleFrontiers in pediatrics2025

Critical conversations: a user-centric approach to chatbots for history taking in the pediatric intensive care unit.

Candace Collins, James Fackler, Melissa Jerdonek Sacco, Maia Jacobs

Abstract read
In one paragraph

Article in Frontiers in pediatrics, 2025. 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. 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

4 authors.

Candace CollinsDepartment of Anesthesiology and Critical Care Medicine, Johns Hopkins Hospital, Baltimore, MD, United States.
James FacklerDepartment of Anesthesiology and Critical Care Medicine, Johns Hopkins Hospital, Baltimore, MD, United States.
Melissa Jerdonek SaccoDepartment of Pediatrics, University of Virginia Health Children's, Charlottesville, VA, United States.
Maia JacobsDepartment of Computer Science, McCormick School of Engineering, Northwester University, Evanston, IL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, we describe the potential utility and design of chatbots to improve history taking in the pediatric intensive care unit (PICU). The fast-paced, high-stakes environment of the PICU often forces clinicians to obtain only enough information to make immediate clinical decisions. Specific barriers to comprehensive history taking include insufficient time, frequent interruptions, caring for a wide range of conditions, need for timely interventions, and language differences. We propose that chatbots could play a critical role in improving history taking in the PICU by collecting information related to a patient's current presentation and exploring areas that are commonly neglected, such as social histories. To explore the use of chatbots in the PICU setting, we will first describe the current scope of chatbots as medical history taking aids. Next, we will outline specific considerations for the development of chatbots for the PICU, including methods for involving users, such as patients, caregivers, and clinicians directly in the design, mitigating false information, and establishing safeguards for chatbot behavior. Finally, we will review methods to evaluate chatbots. The overall purpose of this perspective article is to 1) propose the PICU as a novel environment where chatbots could improve history taking and diagnostic reasoning and 2) delineate specific user-centric design and evaluation methods.

Indexed as

chatbot adoptionclinical reasoningcommunicationdiagnostic reasoningPICU (pediatric intensive care unit)

Identifiers

PMID40873736
PMCPMC12378061

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.