Evidence map›Paper›PMID 38657194›Full record

ArticleJCO clinical cancer informatics2024

Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.

Elizabeth A Sloss, Jordan P McPherson, Anna C Beck, Jia-Wen Guo, Carolyn H Scheese, Naomi R Flake, George Chalkidis, Catherine J Staes

Open access · hybridAbstract read
In one paragraph

Article in JCO clinical cancer informatics, 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
0.2field-weighted citation impact, top 42% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2025
    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

8 authors at 2 institutions in 2 countries.

Elizabeth A SlossCollege of Nursing, University of Utah, Salt Lake City, UT.ORCID 0000-0003-2071-5495
Jordan P McPhersonHuntsman Cancer Institute, University of Utah, Salt Lake City, UT.ORCID 0000-0003-4038-7951
Anna C BeckHuntsman Cancer Institute, University of Utah, Salt Lake City, UT.
Jia-Wen GuoCollege of Nursing, University of Utah, Salt Lake City, UT.ORCID 0000-0002-4698-4696
Carolyn H ScheeseCollege of Nursing, University of Utah, Salt Lake City, UT.ORCID 0009-0005-1281-935X
Naomi R FlakeClinical & Translational Science Institute, University of Utah, Salt Lake City, UT.ORCID 0000-0002-6429-4103
George ChalkidisHitachi Ltd, Tokyo, Japan.ORCID 0000-0003-4414-9010
Catherine J StaesCollege of Nursing, University of Utah, Salt Lake City, UT.ORCID 0000-0002-5423-3251
University of Utah · USHitachi (Japan) · JP

Funding

Utah Center for Clinical and Translational ScienceUL1TR002538 · NCATS · UNIVERSITY OF UTAH · PI HESS, RACHEL, MAJERSIK, JENNIFER JUHL · 2018 to 2022
$26.0M
CTSA UM1 Program at University of UtahUM1TR004409 · NCATS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI RACHEL HESS, Jennifer Juhl Majersik · 2023 to 2026
$21.9M
Interdisciplinary Training in Cancer, Caregiving and End-of-Life CareT32NR013456 · NINR · UNIVERSITY OF UTAH · PI ELLINGTON, LEE A, MOONEY, KATHLEEN H · 2013 to 2022
$3.4M
NCATS NIH HHS UL1 TR002538NINR NIH HHS T32 NR013456
6 · The paper itself

Abstract

purposeUse of artificial intelligence (AI) in cancer care is increasing. What remains unclear is how best to design patient-facing systems that communicate AI output. With oncologist input, we designed an interface that presents patient-specific, machine learning-based 6-month survival prognosis information designed to aid oncology providers in preparing for and discussing prognosis with patients with advanced solid tumors and their caregivers. The primary purpose of this study was to assess patient and caregiver perceptions and identify enhancements of the interface for communicating 6-month survival and other prognosis information when making treatment decisions concerning anticancer and supportive therapy.

methodsThis qualitative study included interviews and focus groups conducted between November and December 2022. Purposive sampling was used to recruit former patients with cancer and/or former caregivers of patients with cancer who had participated in cancer treatment decisions from Utah or elsewhere in the United States. Categories and themes related to perceptions of the interface were identified.

resultsWe received feedback from 20 participants during eight individual interviews and two focus groups, including four cancer survivors, 13 caregivers, and three representing both. Overall, most participants expressed positive perceptions about the tool and identified its value for supporting decision making, feeling less alone, and supporting communication among oncologists, patients, and their caregivers. Participants identified areas for improvement and implementation considerations, particularly that oncologists should share the tool and guide discussions about prognosis with patients who want to receive the information.

conclusionThis study revealed important patient and caregiver perceptions of and enhancements for the proposed interface. Originally designed with input from oncology providers, patient and caregiver participants identified additional interface design recommendations and implementation considerations to support communication about prognosis.

Indexed as

Artificial IntelligenceCaregiversNeoplasmsAdultAgedCommunicationFemaleFocus GroupsHumansMaleMiddle AgedPerceptionPrognosisQualitative ResearchUser-Computer Interface

Identifiers

PMID38657194
PMCPMC11161249
OpenAlexW4395085712

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.