Indicateurs de performance utiles pour soutenir la prise de décision par la médecine de famille dans la lutte contre le cancer
Bibliographic record
Abstract
IT platforms and data-driven performance indicators have become more relevant than ever to provide decision support to all stakeholders in patient care. Healthcare management is complex and faces many challenges to improve its efficiency and performance. The COVID-19 pandemic has shaken health systems around the world. Despite this, cancer remains the leading cause of death and a major health problem in developed countries. Since the first wave of the pandemic in March 2020, many doctors around the world have raised the alarm about delays in the provision of certain care, particularly in cancer diagnosis. Access to data and information can help track the trajectory of oncology patients, however, analyzing it and having performance indicators to guide decision making would improve care delivery. However, the use of performance indicators in the practice of family physicians in Quebec is not well established, nor is it standardized in the daily individual practice of family physicians in Quebec. This work aims to fill this gap in order to provide an overview of relevant indicators that can guide family physicians in their practice, particularly in the fight against cancer. This research provides a portrait of the metrics and performance indicators reported in the literature as well as family physicians' perceptions of performance indicators to guide the care pathway of oncology patients. It contributes to the knowledge in the field of health care management and on the needs of users in the development of decision support tools, particularly in the electronic medical record.
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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 | 0.029 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".