Building a Decentralized Biobanking App for Research Transparency and Patient Engagement: Participatory Design Study
Bibliographic record
Abstract
BACKGROUND: Patient-derived biospecimens are invaluable tools in biomedical research. Currently, there are no mechanisms for patients to follow along and learn about the uses of their donated samples. Incorporating patients as stakeholders and meaningfully engaging them in biomedical research first requires transparency of research activities. OBJECTIVE: In this paper, we describe the use of participatory design methods to build a decentralized biobanking "de-bi" mobile app where patients could learn about biobanking, track their specimens, and engage with ongoing research via patient-friendly interfaces overlaying institutional biobank databases, initially developed for a breast cancer use case. METHODS: This research occurred in 2 phases. In phase 1, we designed app screens from which patients could learn about ongoing research involving their samples. We embedded these screens in a survey (n=94) to gauge patients' interests regarding types of feedback and engagement opportunities; survey responses were probed during 6 comprehensive follow-up interviews. We then held an immersive participatory design workshop where participants (approximately 50) provided general feedback about our approach, with an embedded codesign workshop where a subset (n=15) provided targeted feedback on screen designs. For phase 2, we refined user interfaces and developed a functional app prototype in consultation with institutional stakeholders to ensure regulatory compliance, workflow compatibility, and composability with local data architectures. We presented the app at a second workshop, where participants (n=25, across 9 groups) shared thoughts on the app's usability and design. In this phase, we conducted cognitive walkthroughs (n=13) to gain in-depth feedback on in-app task navigation. RESULTS: Most of the survey participants (61/81, 75%) were interested in learning the outcomes of research on their specimens, and 49% (41/83) were interested in connecting with others with the same diagnosis. Participants (47/60, 78%) expressed strong interest in receiving patient-friendly summaries of scientific information from scientists using their biospecimens. The first design workshop identified confusion in terminology and data presentation (eg, 9/15, 60% of co-designers were unclear on the biospecimens "in use"), though many appreciated the ability to view their personal biospecimens (7/15, 47%), and most were excited about connecting with others (12/15, 80%). In the second workshop, all groups found the app's information valuable. Moreover, 44% (5/9) noted they did not like the onboarding process, which was echoed in cognitive walkthroughs. Walkthroughs further confirmed interest in biospecimen tracking, and 23% (3/13) had confusion about not finding any of their biospecimens in the app. These findings guided refinements in onboarding, design, and user experience. CONCLUSIONS: Designing a patient-facing app that displays information about biobanked specimens can facilitate greater transparency and engagement in biomedical research. Co-designing the app with patient stakeholders confirmed interest in learning about biospecimens and related research, improved presentation of data, and ensured usability of the app in preparation for a pilot study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".