Evidence map›Paper›PMID 41649872›Full record

ArticleJMIR formative research2026

Clinical Decision Support Tool for Early Pancreatic Cancer Detection in Primary Care: Simulation Study.

Javiera Martinez-Gutierrez, Kaleswari Somasundaram, Christina Maresch Bernardes, Meena Rafiq, Silja Schrader, Susan Jordan, Sophie Chima, Lucas De Mendonca, Kit Huckvale, Barbara Hunter and 6 more

Abstract read
In one paragraph

Article in JMIR formative research, 2026. 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
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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

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

16 authors.

Javiera Martinez-Gutierrez *Department of General Practice, Medicine, Dentristry and Health Services, The University of Melbourne, Melbourne, Australia.ORCID 0000-0002-2493-9974
Kaleswari Somasundaram *Department of General Practice, Medicine, Dentristry and Health Services, The University of Melbourne, Melbourne, Australia.ORCID 0000-0002-4330-3453
Christina Maresch BernardesSchool of Public Health, The University of Queensland, Brisbane, Australia.ORCID 0000-0002-8061-7013
Meena RafiqDepartment of General Practice, Medicine, Dentristry and Health Services, The University of Melbourne, Melbourne, Australia.ORCID 0000-0002-1837-1542
Silja SchraderDepartment of General Practice, Medicine, Dentristry and Health Services, The University of Melbourne, Melbourne, Australia.ORCID 0000-0003-1776-6152
Susan JordanSchool of Public Health, The University of Queensland, Brisbane, Australia.ORCID 0000-0002-4566-1414
Sophie ChimaDepartment of General Practice, Medicine, Dentristry and Health Services, The University of Melbourne, Melbourne, Australia.ORCID 0000-0003-1746-5851
Lucas De MendoncaDepartment of General Practice, Medicine, Dentristry and Health Services, The University of Melbourne, Melbourne, Australia.ORCID 0000-0002-6516-9574
Kit HuckvaleThe Digital Health Validitron, Centre for Digital Transformation of Health, The University of Melbourne, Melbourne, Australia.ORCID 0000-0001-9088-6682
Barbara HunterDepartment of General Practice, Medicine, Dentristry and Health Services, The University of Melbourne, Melbourne, Australia.ORCID 0000-0002-1268-3166
Jo-Anne Manski-NankervisDepartment of General Practice, Medicine, Dentristry and Health Services, The University of Melbourne, Melbourne, Australia.ORCID 0000-0003-2153-3482
James LawsonCancer Australia, Sydney, Australia.ORCID 0009-0001-8444-7293
Katrina AndersonCancer Australia, Sydney, Australia.ORCID 0009-0009-7649-766X
Vivienne MilchCancer Australia, Sydney, Australia.ORCID 0000-0001-8834-0349
Rachel E Neale *School of Public Health, The University of Queensland, Brisbane, Australia.ORCID 0000-0001-7162-0854
Jon Emery *Department of General Practice, Medicine, Dentristry and Health Services, The University of Melbourne, Melbourne, Australia.ORCID 0000-0002-5274-6336

Funding

National Health and Medical Research Council APP1195302
6 · The paper itself

Abstract

backgroundEarly detection in primary care could improve pancreatic cancer survival, but diagnosis is often delayed due to the low prevalence of the disease, the nonspecific nature of early symptoms, and the broad range of conditions and volume of consultations managed by general practitioners (GPs). In Australia, improving pancreatic cancer outcomes, including via earlier diagnosis, is a priority being progressed under the National Pancreatic Cancer Roadmap developed by Cancer Australia. Computerized clinical decision support systems (CDSSs) have shown promise in aiding timely cancer diagnosis; however, barriers to adopting CDSS such as mistrust of the recommendations or not being embedded in the clinical workflow remain. Simulation techniques, which offer flexible and cost-effective ways to evaluate digital health interventions, can be used to test CDSS before real-world implementation.

objectiveThis study aims to assess the acceptability and feasibility of identifying patients with symptoms associated with pancreatic cancer through a CDSS within a simulated environment.

methodsWe developed a CDSS that interacted with an electronic health record used in general practice to identify patients with symptoms, which may indicate pancreatic cancer (unintended weight loss or new-onset diabetes), in a simulation laboratory for digital interventions. We tested it by inviting GPs (n=11) to use the CDSS, with patient actors simulating specific clinical scenarios. We then interviewed GPs about the interaction to assess the acceptability and feasibility of the CDSS in their clinical practice. We used thematic analysis and 2 relevant frameworks to analyze the data.

resultsGPs found the CDSS easy to use, unobstructive, and effective as a prompt to consider investigations for people with risk factors for pancreatic cancer. However, they expressed concerns about possible overtesting, financial costs, and the potential for anxiety in patients with a very low probability of having cancer.

conclusionsWhile GPs found the tool useful and compatible with their workflow, concerns about overtesting, lack of evidence, and cost-effectiveness were identified as barriers. GPs favored a stepwise approach to investigations rather than immediate imaging. Despite the overall acceptability of the tool, additional evidence to underpin clinical recommendations is necessary before implementing a CDSS with these specific recommendations for pancreatic cancer in primary care.

Indexed as

Decision Support Systems, ClinicalEarly Detection of CancerPancreatic NeoplasmsAdultAgedAustraliaComputer SimulationFemaleHumansMaleMiddle AgedPrimary Health Carecancerclinical decision supportdigital healthearly detectiongeneral practicesimulation

Identifiers

PMID41649872
PMCPMC12924040

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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.