ArticleJournal of biomedical informatics2017
Veterans Like Me: Formative evaluation of a patient decision aid design.
Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
Who cites it
3 citing papers in PubMed.
- Evaluating the efficacy of NoteAid on EHR note comprehension among US Veterans through Amazon Mechanical Turk.International journal of medical informatics · 2023Trial
- User Perspectives on a Clinical Decision Tool to Support Individualized Exercise Prescriptions for Breast Cancer Survivors Not Meeting Exercise Guidelines: Cross-Sectional Survey.JMIR formative research · 2026Article
- Digital Patient Decision Aid for Antiobesity Medications: Mixed Methods Study of Human-Centered Design and Usability Evaluation.Journal of medical Internet research · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Patient decision aids are tools intended to facilitate shared decision-making. Currently development of a patient decision aid is resource intensive: it requires a decision-specific review of the scientific literature by experts to ascertain the potential outcomes under different treatments. The goal of this project was to conduct a formative evaluation of a generalizable, scalable decision aid component we call Veterans Like Me (VLme). VLme mines EHR data to present the outcomes of individuals "like you" on different treatments to the user. These outcome are presented through a combination of an icon array and simulated narratives. Twenty-six patients participated in semi-structured interviews intended to elicit feedback on the tool's functional and interface design. The interview focused on the filters users desired with which to make cases similar to them, the kinds of outcomes they wanted presented, and their envisioned use of the tool. The interview also elicited participants information needs and salient factors related to the therapeutic decision. The interview transcripts were analyzed using an iteratively refined coding schema and content analysis. . Participants generally expressed enthusiasm for the tool's design and functionality. Our analysis identified desired filters for users to view patients like themselves, outcome types that should be included in future iterations of the tool (e.g. patient reported outcomes), and information needs that need to be addressed for patients to effectively participate in shared decision making. Implications for the integration of our findings into the design of patient decision aids are discussed.
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What OpenQuestion holds
Registered trials
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.