Assessing the performance of centralized waiting lists for patients without a regular family physician using clinical-administrative data
Bibliographic record
Abstract
BACKGROUND: With 4.6 million patients who do not have a regular family physician, Canada performs poorly compared to other OECD countries in terms of attachment to a family physician. To address this issue, several provinces have implemented centralized waiting lists to coordinate supply and demand for attachment to a family physician. Although significant resources are invested in these centralized waiting lists, no studies have measured their performance. In this article, we present a performance assessment of centralized waiting lists for unattached patients implemented in Quebec, Canada. METHODS: We based our approach on the Balanced Scorecard method. A committee of decision-makers, managers, healthcare professionals, and researchers selected five indicators for the performance assessment of centralized waiting lists, including both process and outcome indicators. We analyzed and compared clinical-administrative data from 86 centralized waiting lists (GACOs) located in 14 regions in Quebec, from April 1, 2013, to March 31, 2014. RESULTS: During the study period, although over 150,000 patients were attached to a family physician, new requests resulted in a 30% median increase in patients on waiting lists. An inverse correlation of average strength was found between the rates of patients attached to a family physician and the proportion of vulnerable patients attached to a family physician meaning that as more patients became attached to an FP through GACOs, the proportion of vulnerable patients became smaller (r = -0.31, p < 0.005). The results showed very large performance variations both among GACOs of different regions and among those of a same region for all performance indicators. CONCLUSIONS: Centralized waiting lists for unattached patients in Quebec seem to be achieving their twofold objective of attaching patients to a family physician and giving priority to vulnerable patients. However, the demand for attachment seems to exceed the supply and there appears to be a tension between giving priority to vulnerable patients and attaching of a large number of patients. Results also showed heterogeneity in the performance of centralized waiting lists across Quebec. Finally, our findings suggest it is critical that similar mechanisms should use available data to identify the best strategies for reducing variations and improving performance.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".