Communicating Patient Health Data: A Wicked Problem
Bibliographic record
Abstract
Designing patient-collected health data visualizations to support communicating patient data during clinical visits is a challenging problem due to the heterogeneity of the parties involved: patients, healthcare providers, and healthcare systems. Designers must ensure that all parties' needs are met. This complexity makes it challenging to find a definitive solution that can work for every individual. We have approached this research problem-communicating patient data during clinical visits-as a wicked problem. In this article, we outline how wicked problem characteristics apply to our research problem. We then describe the research methodologies we employed to explore the design space of individualized patient data visualization solutions. Lastly, we reflect on the insights and experiences we gained through this exploratory design process. We conclude with a call to action for researchers and visualization designers to consider patients' and healthcare providers' individualities when designing patient data visualizations.
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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 | 0.126 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".