High and Sustained Participation in a Multi-year Voluntary Performance Measurement Initiative Among Primary Care Teams
Bibliographic record
Abstract
BACKGROUND: The province of Ontario, Canada has made major investments in interdisciplinary primary care teams. There is interest in both demonstrating and improving the quality of care they provide. Challenges include lack of consensus on the definition of quality and evidence that the process of measuring quality can be counter-productive to actually achieving it. This study describes how primary care teams in Ontario voluntarily measured quality at the team level. METHODS: Data for this 4-year observational study came from electronic medical records (EMRs), patient surveys and administrative reports. Descriptive statistics were calculated for individual measures (eg, access, preventive interventions) and composite indicators of quality and healthcare system costs. Repeated measures identified patient and practice characteristics related to quality and cost outcomes. RESULTS: Teams participated in an average of 5 of 8 possible iterations of the reporting process. There was variation between teams. For example, cervical cancer screening rates ranged from 21 to 86% of eligible patients. Rural teams had significantly better performance on some indicators (eg, continuity) and worse on others (eg, cancer screening). There were some statistical but small changes in performance over time. CONCLUSION: High, sustained voluntary participation suggests that the initiative served a need for the primary care teams involved. The absence of robust data standards suggests that these standards were not crucial to achieve participation. The constant level of performance might mean that measurement has not yet led to improvement or that measures used might not accurately reflect improvement. The data reinforce the need to consider differences between rural and urban settings. They also suggest that further analysis is needed to identify characteristics that teams can change to improve the quality of care their patients experience. The study describes a practical, sustainable real-world approach to performance measurement in primary care that was attractive to interdisciplinary teams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".