Evidence map›Paper›PMID 41032743›Full record

ArticleJCO clinical cancer informatics2025

Unsupervised Large Language Models to Identify Topics in Cancer Center Patient Portal Messages.

Ji Hyun Chang, Amir Ashraf-Ganjouei, Isabel Friesner, Ryzen Benson, Travis Zack, Sumi Sinha, Jason Chan, Steve Braunstein, Amy Lin, Lisa Singer and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Ji Hyun ChangBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Amir Ashraf-GanjoueiBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Isabel FriesnerBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0009-0000-0216-4031
Ryzen BensonBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Travis ZackBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0002-1620-6455
Sumi SinhaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0002-9705-0340
Jason ChanBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0002-3152-1023
Steve BraunsteinBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0002-9841-9357
Amy LinBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Lisa SingerBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Julian C HongBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0001-5172-6889

Funding

Multi-institutional validation of a multi-modal machine learning algorithm to predict and reduce acute care during cancer therapyR01CA277782 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Julian Clint Hong · 2023 to 2026
$1.6M
NCI NIH HHS R01 CA277782
6 · The paper itself

Abstract

purposeThe increasing use of patient portal messages has enhanced patient-provider communication. However, the high volume of these messages has also contributed to physician burnout.

methodsPatient-generated portal messages sent to a single cancer center from 2011 to 2023 were extracted. BERTopic, a natural language processing topic modeling technique based on large language models, was optimized. For further categorization, the topic words were labeled using GPT-4, followed by review by two oncologists. Uniform Manifold Approximation and Projection was used for dimensionality reduction and visualizing topics. Message volume changes over time were assessed using a Student's

resultsA total of 2,280,851 messages were analyzed. The monthly average number of messages increased from 2,071 in 2012 to 43,430 in 2022 (

conclusionThe substantial increase in patient portal messages, particularly scheduling-related inquiries, underscores the need for streamlined communication to reduce the burden on health care providers. These findings highlight the need for strategies to manage message volume and mitigate physician burnout, laying groundwork for artificial intelligence-driven future triage systems to improve message management and patient care.

Indexed as

Cancer Care FacilitiesCOVID-19Natural Language ProcessingNeoplasmsPatient PortalsHumansLarge Language ModelsMedical OncologySARS-CoV-2

Identifiers

PMID41032743
PMCPMC12490804

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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.