Design and feasibility of integrating personalized PRO dashboards into prostate cancer care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Patient-reported outcomes (PROs) are a valued source of health information, but prior work focuses largely on data capture without guidance on visual displays that promote effective PRO use in patient-centered care. We engaged patients, providers, and design experts in human-centered design of "PRO dashboards" that illustrate trends in health-related quality of life (HRQOL) reported by patients following prostate cancer treatment. MATERIALS AND METHODS: We designed and assessed the feasibility of integrating dashboards into care in 3 steps: (1) capture PRO needs of patients and providers through focus groups and interviews; (2) iteratively build and refine a prototype dashboard; and (3) pilot test dashboards with patients and their provider during follow-up care. RESULTS: Focus groups (n = 60 patients) prioritized needs for dashboards that compared longitudinal trends in patients' HRQOL with "men like me." Of the candidate dashboard designs, 50 patients and 50 providers rated pictographs less helpful than bar charts, line graphs, or tables (P < .001) and preferred bar charts and line graphs most. Given these needs and the design recommendations from our Patient Advisory Board (n = 7) and design experts (n = 7), we built and refined a prototype that charts patients' HRQOL compared with age- and treatment-matched patients in personalized dashboards. Pilot testing dashboard use (n = 12 patients) improved compliance with quality indicators for prostate cancer care (P < .01). CONCLUSION: PRO dashboards are a promising approach for integrating patient-generated data into prostate cancer care. Informed by human-centered design principles, this work establishes guidance on dashboard content, tailoring, and clinical use that patients and providers find meaningful.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it