Personal Health Record Software for Neuroendocrine Tumors: Patient-Centered Design Approach
Bibliographic record
Abstract
Background: Personal health record (PHR) software has the potential of aiding with patient engagement and data collection in longitudinal research to better understand the long-term impact of treatments on patients with rare medical conditions. Neuroendocrine tumors (NETs) represent a rare condition with unique challenges related to symptom management, treatment tracking, and patient-provider communication. Objective: This study aimed to design, develop, and evaluate PHR software tailored for patients with NETs as part of a longitudinal research study. Our goal was to create a patient-centered PHR that supports both self-management and research data collection. Methods: This included activities spanning the entire development lifecycle from identifying user requirements through focus groups and surveys, iterative prototype refinement via cognitive walkthroughs, and usability testing of the functional PHR system. Feedback from patient advocacy organizations and clinical experts further informed PHR development. Results: The resulting PHR allows patients with NETs to access condition-specific information, track symptoms, monitor treatment regimens, and share data with health care providers. Patients valued the ability to visualize personal health trends and patterns over time, enhancing both self-management and communication with medical teams. Usability testing indicated high levels of patient satisfaction with the system's functionality and design. Conclusions: The development of this PHR demonstrates the value of engaging patients in the design process to ensure that health technologies address real-world needs. Our approach provides a model for designing PHR systems for other rare conditions, highlighting the importance of patient-centered design in supporting both clinical care and longitudinal research.
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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 | 0.031 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".