Toward a provincial dashboard on accessibility and continuity in primary health care based on electronic medical records
Bibliographic record
Abstract
Context A strong primary health care (PHC) system requires access to data to orient evidence-based optimization of services, which has been challenging. Dashboards are quality improvement tools that have the power to enable optimization of the performance of health organizations and support timely decision-making based on data made available in real time. Objective To present a set of key indicators of accessibility and relational continuity to cultivate a PHC dashboard based on EMRs. Study Design Sequential multi-method design informed by (1) a systematic review of indicators and validation through expert consultations (n=17), (2) a pilot phase of data extraction from EMRs and (3) the development of a dashboard to compare data over time, both among PHC providers within a clinic and between clinics. Setting Primary healthcare settings in Quebec. Population studied: (1) A committee of 17 experts represented provincial and local decision-makers, PHC clinic members (family physicians, nurses and administrative staff), patients and researchers. (2) Eight medical clinics participated in the extraction pilot. (3) A total of 114 medical clinics across the province agreed to contribute to the dashboard. Results Of the 1733 citations found through the search strategy, 81 scientific papers covering 12 access indicators were included in the review. Seven additional indicators were added through consultation with the expert group. Eight indicators were then prioritized and successfully extracted from EMRs for 151 PHC providers (physicians, nurse practitioners and nurses): 1) third next available appointment, 2) relational continuity, 3) 48-hour capacity, 4) walk-in appointment utilization rate, 5) professional diversity of care, 6) no-show rate, 7) patient demand, and 8) provider supply. Indicators were extracted on a daily or monthly basis (depending on the variability of the indicator). The dashboard, composed of eight indicators, has been created, and the data are accessible in real time. Comparisons of indicators on PHC over time, as well as among providers and among clinics, have been enabled in the dashboard. Conclusion The creation of a dashboard is an essential component for improving PHC based on key indicators in real time and for providing feedback for professionals and decision-makers in quality improvement projects.
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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 | 0.094 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".