Evidence map›Paper›PMID 42186048›Full record

ArticleBMC health services research2026

An exploratory mixed-methods study using the ELICIT framework to identify stakeholder-informed implementation strategies for a population health platform.

Anne C Madeo, Jarrod Marable, Polina Kukhareva, Wendy K Kohlmann, Richard L Bradshaw, Whitney Maxwell, Che Martin, Lauren Davis-Rivera, Muhammad Danyal Ahsan, Emerson P Borsato and 8 more

Abstract read
In one paragraph

Article in BMC health services 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
–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

18 authors.

Anne C MadeoDepartment of Population Health Sciences, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA. Anne.madeo@utah.edu.
Jarrod MarableDepartment of Implementation Science, Wake Forest University School of Medicine, Winston Salem, NC, USA.
Polina KukharevaDepartment of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA.
Wendy K KohlmannHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
Richard L BradshawDepartment of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA.
Whitney MaxwellHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
Che MartinDepartment of Informatics, New York Presbyterian Hospital, New York, NY, USA.
Lauren Davis-RiveraGenetics and Personalized Cancer Prevention Program, Weill Cornell Medicine, New York, NY, USA.
Muhammad Danyal AhsanGenetics and Personalized Cancer Prevention Program, Weill Cornell Medicine, New York, NY, USA.
Emerson P BorsatoDepartment of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA.
Elena B ElkinDepartment of Health Policy and Management, Columbia University Mailman School of Public Health, New York, NY, USA.
Kimberly A KaphingstHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
Melissa K FreyGenetics and Personalized Cancer Prevention Program, Weill Cornell Medicine, New York, NY, USA.
Ravi N SharafGenetics and Personalized Cancer Prevention Program, Weill Cornell Medicine, New York, NY, USA.
Kensaku KawamotoDepartment of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA.
Guilherme Del FiolDepartment of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA.
Chelsey Schlechter *Department of Population Health Sciences, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA.
Caitlin G Allen *Department of Implementation Science, Wake Forest University School of Medicine, Winston Salem, NC, USA.

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

backgroundGARDE is an open-source software platform that supports population health management interventions including hereditary cancer cohort identification, patient engagement, education, and access to genetic testing services. To facilitate GARDE implementation, we applied the Evaluation in Life Cycle of Information Technology framework to 1) identify barriers and facilitators to the implementation of GARDE at five healthcare systems in differing implementation phases, 2) develop implementation strategies to address barriers, and 3) prioritize implementation strategies for implementation toolkit development based on partner-rated feasibility and importance.

methodsWe facilitated focus group discussions and individual in-depth interviews (n = 17) at five healthcare systems that have implemented or are implementing GARDE to identify contextual determinants of implementation and strategies to address implementation barriers. We relied on the Evaluation in Life Cycle of Information Technology framework to develop the interview guide and analyze data using rapid qualitative techniques. Our team identified implementation strategies in response to reported barriers. GARDE implementers were surveyed to assess the feasibility and importance of implementation strategies.

resultsParticipants at all healthcare systems identified multiple GARDE implementation barriers and facilitators, largely focusing on information technology needs to effectively implement an electronic health record-integrated innovation. Eight implementation strategies were identified as both important and feasible to address identified barriers.

conclusionsPopulation health management innovations are a potential solution to healthcare system challenges through cohort identification, patient engagement, and access to relevant healthcare services, but their implementation must meet multiple requirements. This study of an EHR-integrated population health management platform implementation used an Evaluation in Life Cycle of Information Technology -grounded approach to identify implementation barriers and facilitators and implementation strategies to address barriers. An implementation toolkit is being refined to support the implementation and dissemination of population health management platforms.

Indexed as

Population HealthPopulation Health ManagementSoftwareStakeholder ParticipationDigital HealthElectronic Health RecordsFocus GroupsGenetic TestingHumansInterviews as TopicQualitative ResearchElectronic health recordsFocus groupsGenetic predisposition to diseaseHumansImplementation scienceInformation technologyPopulation health managementTesting, genetic predisposition

Identifiers

PMID42186048
PMCPMC13317325

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.