Getting results for hematology patients through access to the electronic health record
Bibliographic record
Abstract
PURPOSE: To conduct a needs assessment to identify patient and provider perceptions about providing patients with access to their electronic health record in order to develop an online system that is appropriate for all stakeholders. METHODS: Malignant hematology patients were surveyed and health care providers were interviewed to identify issues and validate concerns reported in the literature. Based on the analysed data, a prototype will be designed to examine the feasibility and efficacy of providing patients with access to their electronic health record and tailored information. RESULTS: 61% of patients reported using the internet to find health information; 89% were interested in accessing their electronic health record and 79% stated they would benefit from educational material along with the results. Staff members viewed patient online access to the record favourably, but expressed the importance of providing the necessary patient support and education. A Web-based prototype was developed for patients to review their registration data and blood results. CONCLUSIONS: Hematology oncology patients are more interested in using the internet to monitor their clinical information than to find health information. Using the constructed prototype, the feasibility of this project is currently being tested.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".