Evidence map›Paper›PMID 41843856›Full record

ArticleJCO oncology practice2026

Economic Evaluation of GARDE: A Digital Health Platform for Population-Level Hereditary Cancer Risk Assessment.

Muhammad Danyal Ahsan, Kimberly A Kaphingst, Wendy K Kohlmann, Richard L Bradshaw, Caitlin G Allen, Chelsey Schlechter, Polina Kukhareva, Whitney Maxwell, Che Martin, Lauren Davis-Rivera and 7 more

Abstract read
In one paragraph

Article in JCO oncology practice, 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
–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

17 authors.

Muhammad Danyal AhsanGenetics and Personalized Cancer Prevention Program, Weill Cornell Medicine, New York, NY.ORCID 0000-0001-8633-8931
Kimberly A KaphingstHuntsman Cancer Institute, University of Utah, Salt Lake City, UT.ORCID 0000-0003-2668-9080
Wendy K KohlmannHuntsman Cancer Institute, University of Utah, Salt Lake City, UT.ORCID 0000-0002-9134-9640
Richard L BradshawDepartment of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT.ORCID 0000-0001-7363-0327
Caitlin G AllenDepartment of Public Health Sciences, Medical University of South Carolina, Charleston, SC.ORCID 0000-0002-6288-3529
Chelsey SchlechterDepartment of Population Health Sciences, University of Utah, Salt Lake City, UT.
Polina KukharevaDepartment of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT.ORCID 0000-0002-5576-1486
Whitney MaxwellHuntsman Cancer Institute, University of Utah, Salt Lake City, UT.
Che MartinDepartment of Informatics, New York Presbytarian Hospital, New York, NY.ORCID 0000-0002-7582-3103
Lauren Davis-RiveraGenetics and Personalized Cancer Prevention Program, Weill Cornell Medicine, New York, NY.
Anne C MadeoHuntsman Cancer Institute, University of Utah, Salt Lake City, UT.ORCID 0000-0003-2048-9491
Emerson P BorsatoDepartment of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT.
Melissa K FreyGenetics and Personalized Cancer Prevention Program, Weill Cornell Medicine, New York, NY.ORCID 0000-0002-6705-1211
Kensaku KawamotoDepartment of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT.ORCID 0000-0003-4282-9338
Guilherme Del FiolDepartment of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT.ORCID 0000-0001-9954-6799
Elena B ElkinDepartment of Health Policy and Management, Columbia University Mailman School of Public Health, New York, NY.ORCID 0000-0001-8833-4213
Ravi N SharafGenetics and Personalized Cancer Prevention Program, Weill Cornell Medicine, New York, NY.ORCID 0000-0002-6905-9823

Funding

GARDE: Scalable Clinical Decision Support for Individualized Cancer Risk ManagementU24CA274582 · NCI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI GUILHERME DEL FIOL, Kensaku Kawamoto · 2023 to 2026
$3.3M
NCI NIH HHS U24 CA274582
6 · The paper itself

Abstract

purposeOver 90% of people with hereditary cancer syndromes in the United States remain unidentified. The Genetic Cancer Risk Detector (GARDE) is an open-source, electronic health record (EHR)-integrated, digital health platform that can facilitate genetic cancer risk assessment and genetic testing. This study evaluates its budget impact on health care institutions.

methodsA budget impact analysis was performed from the perspective of a US health care provider system over a 3-year horizon. Data from the BRIDGE randomized controlled trial data from the University of Utah Health (UHealth) were used, where eligible primary care patients were screened for genetic cancer risk via GARDE. Costs of GARDE were categorized across planning, implementation, and operational phases. Revenue projections were based on Centers for Medicare & Medicaid Services reimbursement rates. Scenario analyses varied uptake of interventions, surveillance intervals, reimbursement rates, and implementation scale.

resultsOf 1,444 patients identified by GARDE at UHealth and enrolled in the BRIDGE trial, 205 completed genetic testing, with 15 found to carry pathogenic variants. The total 3-year implementation cost was $29,217 US dollars (USD). Revenue from guideline-recommended procedures totaled $86,563 USD, yielding a net positive budget impact of $57,347 USD. Most revenue (76.4%) was generated by surgical risk-reduction procedures. Scenario analyses revealed high sensitivity to cancer risk-reducing surgery uptake and implementation scale. Modeling 100% uptake of risk-reducing surgeries increased revenue to $128,102 USD, while 20-fold scaling of the implementation population increased revenue to $1.7 million USD. Commercial insurance reimbursement assumptions further amplified revenue.

conclusionGARDE enables scalable hereditary cancer risk assessment within a health care provider system. Even with modest uptake, it yields a positive financial return, and significantly greater revenue is achievable with broader implementation. These findings support adoption of EHR-integrated tools to enhance clinical outcomes in precision cancer prevention and risk management, in an economically viable manner.

Identifiers

PMID41843856
PMCPMC13020674

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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.