Evidence map›Paper›PMID 39725711›Full record

ReviewNPJ digital medicine2024

Contrasting rule and machine learning based digital self triage systems in the USA.

Bilal A Naved, Yuan Luo

Abstract readReview
In one paragraph

Review in NPJ digital medicine, 2024. 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. 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

2 authors.

Bilal A NavedDepartment of Biomedical Engineering, Northwestern University McCormick School of Engineering, Chicago, IL, USA.
Yuan LuoDepartment of Preventative Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. yuan.luo@northwestern.edu.ORCID http://orcid.org/0000-0003-0195-7456

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM008152 · NIGMS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ARDEHALI, HOSSEIN · 1987 to 2021
$24.0M
Modeling the Incompleteness and Biases of Health DataR01LM013337 · NLM · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LUO, YUAN · 2020 to 2023
$1.3M
Strategies in Renal Nanomedicine to Impact Treatment Paradigms in Kidney DiseaseF30DK123985 · NIDDK · NORTHWESTERN UNIVERSITY AT CHICAGO · PI NAVED, BILAL ABDULLAH · 2019 to 2023
$238k
NIDDK NIH HHS F30 DK123985NIGMS NIH HHS T32 GM008152NLM NIH HHS R01 LM013337U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) F30DK123985U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) T32GM008152U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM) R01LM013337
6 · The paper itself

Abstract

Patient smart access and self-triage systems have been in development for decades. As of now, no LLM for processing self-reported patient data has been published by health systems. Many expert systems and computational models have been released to millions. This review is the first to summarize progress in the field including an analysis of the exact self-triage solutions available on the websites of 647 health systems in the USA.

Identifiers

PMID39725711
PMCPMC11671541

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.