Meeting the Health Information Needs of Prostate Cancer Patients Using Personal Health Records
Bibliographic record
Abstract
BACKGROUND: There is interest in the use of health information technology in the form of personal health record (phr) systems to support patient needs for health information, care, and decision-making, particularly for patients with distressing, chronic diseases such as prostate cancer (pca). We sought feedback from pca patients who used a phr. METHODS: For 6 months, 22 pca patients in various phases of care at the BC Cancer Agency (bcca) were given access to a secure Web-based phr called provider, which they could use to view their medical records and use a set of support tools. Feedback was obtained using an end-of-study survey on usability, satisfaction, and concerns with provider. Site activity was recorded to assess usage patterns. RESULTS: Of the 17 patients who completed the study, 29% encountered some minor difficulties using provider. No security breaches were known to have occurred. The two most commonly accessed medical records were laboratory test results and transcribed doctor's notes. Of survey respondents, 94% were satisfied with the access to their medical records, 65% said that provider helped to answer their questions, 77% felt that their privacy and confidentiality were preserved, 65% felt that using provider helped them to communicate better with their physicians, 83% found new and useful information that they would not have received by talking to their health care providers, and 88% said that they would continue to use provider. CONCLUSIONS: Our results support the notion that phrs can provide cancer patients with timely access to their medical records and health information, and can assist in communication with health care providers, in knowledge generation, and in patient empowerment.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".