A Successful Strategy Addressing Wait Time and Matching Patient's to their Records
Bibliographic record
Abstract
Matching patients and healthcare providers has new importance as nations embrace electronic health records (EHRs) and define strategies for enhancing healthcare delivery. Silos of data are broken down in the new electronic world, striving for increased patient safety, better customer service, and more cost-effective healthcare. Long wait lists prove the bane of customer satisfaction, government policy, and effective use of resources. Matching patients to their records across the data silos is fundamental, while maintaining the integrity, confidentiality, and security of information. The Province of Ontario developed a strategy to reduce wait times for select procedures, addressing a chronic problem of long waits for diagnostic or surgery modalities. Ontario developed a provincial Wait Times Information System (WTIS), capturing data critical to monitoring and determining best use of resources. Now used by 1700 hundred physicians at over 60 sites, WTIS has contributed to significantly reduce wait times for cancer surgery, sight restoration, hip and knee replacement, cardiac surgery and MRI/CT - which is all now publicly reported on a regular basis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".