Evidence map›Paper›PMID 42463061›Full record

ArticleJournal of pain and symptom management2026

Palliative Care Physicians' Perceptions about Using Artificial Intelligence for Prognostication.

Stacy M Fischer, Regina M Fink, Ahmed Y Alasmar, Eric G Campbell, Matthew DeCamp

Abstract read
In one paragraph

Article in Journal of pain and symptom management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

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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

5 authors.

Stacy M FischerDivision of General Internal Medicine (S.M.F., R.M.F., E.G.C., M.D.C.), University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Regina M FinkDivision of General Internal Medicine (S.M.F., R.M.F., E.G.C., M.D.C.), University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA; College of Nursing (R.M.F.), University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Ahmed Y AlasmarCenter for Bioethics and Humanities (A.Y.A., E.G.C., M.D.C.), University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Eric G CampbellDivision of General Internal Medicine (S.M.F., R.M.F., E.G.C., M.D.C.), University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA; Center for Bioethics and Humanities (A.Y.A., E.G.C., M.D.C.), University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Matthew DeCampDivision of General Internal Medicine (S.M.F., R.M.F., E.G.C., M.D.C.), University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA; Center for Bioethics and Humanities (A.Y.A., E.G.C., M.D.C.), University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA. Electronic address: Matthew.decamp@cuanschutz.edu.

Funding

Palliative Care Research Cooperative Group (PCRC): Refinement and ExpansionU2CNR014637 · NINR · UNIVERSITY OF COLORADO DENVER · PI HANSON, LAURA C · 2018 to 2022
$8.6M
A mixed-methods study of the nature, extent and consequences of artificial intelligence (AI) for individualized treatment planning in end-of-life and palliative care (EOLPC)R01NR019782 · NINR · UNIVERSITY OF COLORADO DENVER · PI DECAMP, MATTHEW WAYNE · 2022 to 2025
$2.6M
NINR NIH HHS R01 NR019782NINR NIH HHS U2C NR014637
6 · The paper itself

Abstract

contextStatistical and artificial intelligence (AI)-based methods have informed clinical prognostication for decades, evolving into machine learning models integrated into electronic health records. Despite increasing deployment of AI-based prognostic tools, palliative care physicians' perceptions of these tools remain understudied.

objectivesTo understand palliative care physician perspectives on clinical use and implementation of AI-based prognostic tools.

methodsWe conducted a national survey of n = 2500 Hospice and Palliative Medicine physicians in the United States (January 2024-March 2025) to assess current prognostic practices, AI knowledge, and perceived benefits and risks of AI-based prognostication.

resultsAbout 537 completed surveys were included for analysis. Respondents were predominantly White (73.2%) and female (52.9%). Most reported being early technology adopters (69%) with low knowledge of AI (79.3%) and AI-based prognostication (91.8%). Overall, 72.9% routinely provide prognoses; 24.0% have used AI-generated prognoses at least once. Attitudes toward AI were favorable: 70.9% believed AI could facilitate earlier palliative care, 78.0% thought it could improve patients' ability to plan for end-of-life, 62.8% supported its role in hospice eligibility decisions, and 71.7% felt it might reduce team disagreements about prognosis. However, 36.3% were concerned about legal liability, and 37.5% thought it might negatively affect patients' sense of hope. Multivariate analyses found current users were more likely to hold positive beliefs (e.g., AI will help them better meet their patients' needs (aOR: 1.75; CI: 1.09-2.92; P = 0.026).

conclusionsPalliative care physicians report limited current use of AI-based prognostic tools but generally favorable attitudes toward potential benefits, especially among current AI tool users.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelPalliative CarePhysiciansAdultFemaleHumansMaleMiddle AgedPrognosisSurveys and QuestionnairesUnited StatesArtificial intelligencepalliative careprognostication

Identifiers

PMID42463061
PMCPMC13629606

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.