Routine Collection of Patient-Reported Data to Support the Needs of Primary Care Within an Integrated Healthcare System
Bibliographic record
Abstract
Ontario Health Teams (OHTs), models of integrated care, are responsible for measuring and improving patient experience. However, routine collection of patient-reported data has not been fully realized, presenting a significant system-wide gap. We conducted a pilot study to implement routine collection of patient-reported data in the Frontenac, Lennox and Addington (FLA) OHT. Each clinic integrated the survey, which captured encounter experience, health and well-being and demographics into their workflow. During the five-month pilot, over 1,200 patients shared their experiences. Clinics reported that the data were valuable for ongoing quality improvement, boosting staff morale and providing a voice to patients. Each site needed flexibility for deployment and to ensure that they captured data relevant to their practice needs. A balance is needed to meet differing needs at each level of the system, requiring cross-sectoral commitment for integrated care systems to truly understand the patient experience and health of the population.
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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 | 0.049 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".