Co-Designing a Patient-Facing Dashboard with Patients and Healthcare Providers: Gathering User Input
Bibliographic record
Abstract
Objectives To co-design with rheumatology patients, healthcare providers, and human-centered design experts, a patient-facing dashboard that displays patient-reported outcome measures (PROMs) and clinician-derived data over time. Methods We recruited participants from the Rheum4U Precision Health Registry (PHR) to participate in semi-structured interviews or focus group sessions. Purposive sampling was used based on type of inflammatory arthritis (IA), sex, and geographic location. All the participants engaged in a card-sorting activity in which they sorted the content collected from the Rheum4U PHR’s web-based platform based on what they wanted to see included in the dashboard. During each session, the participants’ preferences of the content and features of dashboard, and the use of the dashboard were explored. The card-sorting data were analyzed using content analysis. Transcripts were analyzed using thematic analysis with NVivo software, guided by the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) and Practical, Robust, Implementation and Sustainability Model (PRISM) frameworks.[1,2] The data from the card-sorting activity and interviews and focus groups were used to inform human-centered design experts in creating a low-fidelity mock-up of the dashboard in an interface design tool, Figma.[3] Results Six patients, 4 nurses, and 3 rheumatologists participated in the sessions. The card-sorting activity revealed the prioritized content deemed most beneficial by participants including disease activity (eg, CDAI, BASDAI, DAS28), physician global, health assessment questionnaire (HAQ) score, pain intensity, and level of fatigue. Key themes emerging from the thematic analysis included: 1. Clear visual representations of longitudinal data trends and comparison over time. 2. Insights into treatment effectiveness. 3. User-friendly navigation of the dashboard. 4. integration with the electronic medical record system. 5. Integrating educational resources relevant to their IA. 6. Alerts for worsening PROMs. 7. Mobile access to the dashboard for patients. The mock-up of the dashboard based on these themes is displayed in Figure 1. Conclusion Co-designing a patient-facing dashboard by patients and healthcare providers supports the identification of priority for intuitive data visualization of personalized health metrics and for integrating resources to support understanding of disease progression to guide care needs. A collaborative approach to the construction of dashboard enhances the potential for all users to benefit once the tool is available. [1.] Glasgow RE. Front Public Health 2019;7:64. [2.] Feldstein AC. Jt Comm J Qual Patient Saf 2008;34(4):228-43. [3.] Figma. https://www.figma.com . Supported by a CIORA grant
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".