Design and Development of a Person-Centered Patient Portal Using Participatory Stakeholder Co-Design (Preprint)
Bibliographic record
Abstract
BACKGROUND Patient portals are increasingly accepted as part of standard medical care. However, to date, most patient portals provide just passive access to medical data. The use of modern technology such as smartphones and data personalization algorithms offers the potential to make patient portals more person-centered and enabling. OBJECTIVE The aim of this study is to share our experience in designing and developing a person-centered patient portal following a participatory stakeholder co-design approach. METHODS Our stakeholder co-design approach comprised 6 core elements: (1) equal coleadership, including a cancer patient on treatment; (2) patient preference determination; (3) security, governance, and legal input; (4) continuous user evaluation and feedback; (5) continuous staff input; and (6) end-user testing. We incorporated person-centeredness by recognizing that patients should decide for themselves their level of medical data access, all medical data should be contextualized with explanatory content, and patient educational material should be personalized and timely. RESULTS Using stakeholder co-design, we built, and are currently pilot-testing, a person-centered patient portal smartphone app called Opal. CONCLUSIONS Inclusion of all stakeholders in the design and development of patient-facing software can help ensure that the necessary elements of person-centeredness, clinician acceptability, and informatics feasibility are achieved.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".