Evidence map›Paper›PMID 39903169›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2025

Using dataflow diagrams to support research informed consent data management communications: participant perspectives.

Brian J McInnis, Ramona Pindus, Daniah H Kareem, Julie Cakici, Daniela G Vital, Eric Hekler, Camille Nebeker

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Brian J McInnisSchool of Information, University of Texas Austin, Austin, TX 78712, United States.
Ramona PindusHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, United States.
Daniah H KareemHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, United States.
Julie CakiciHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, United States.
Daniela G VitalHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, United States.
Eric HeklerHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, United States.
Camille NebekerHerbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, United States.

Funding

Ethics Core (FABRIC)U54HG012510 · NHGRI · YALE UNIVERSITY · PI MALIN, BRADLEY A. · 2022 to 2025
$11.2M
Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering MethodsR01CA244777 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HEKLER, ERIC, RIVERA, DANIEL E · 2020 to 2024
$3.8M
National Science Foundation 2124975NCI NIH HHS R01 CA244777NHGRI NIH HHS U54 HG012510NIH HHS R01CA244777Patient-Centered Outcomes Research Institute ME-2020C3-21310
6 · The paper itself

Abstract

objectivesDigital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent. MATERIALS AND

methodsWe conducted eight semi-structured focus groups to explore whether dataflow diagrams (DFD) can complement informed consent and improve participants' understanding of data management and associated risks (N = 34 participants).

resultsOur analysis found that DFDs could supplement text-based information about data management and sharing practices, such as by helping raise new questions that prompt conversation between prospective participants and members of a research team. Participants in the study emphasized the need for clear, simple, and accessible diagrams that are participant centered. Third-party access to data and sharing of sensitive health data were identified as high-risk areas requiring thorough explanation. Participants generally agreed that the design process should be led by the research team, but it should incorporate many diverse perspectives to ensure the diagram was meaningful to potential participants who are likely unfamiliar with data management. Nearly all participants rejected the idea that artificial intelligence could identify risks during the design process, but most were comfortable with it being used as a tool to format and simplify the diagram. In short, DFDs may complement standard text-based informed consent documents, but they are not a replacement. DISCUSSION: Prospective research participants value diverse ways of learning about study risks and benefits. Our study highlights the value of incorporating information visualizations, such as DFDs, into the informed consent procedures to participate in research.

conclusionFuture research should explore other ways of visualizing consent information in ways that help people to overcome digital and data literacy barriers to participating in research. However, creating a DFD requires significant time and effort from research teams. To alleviate these costs, research sponsors can support the creation of shared infrastructure, communities of practice, and incentivize researchers to develop better consent procedures.

Indexed as

Data ManagementInformed ConsentAdultBiomedical ResearchCommunicationFemaleFocus GroupsHumansMaleMiddle Ageddataflow diagramdata managementdigital healthinformed consentprivacyresearch ethics

Identifiers

PMID39903169
PMCPMC12005621

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.