Looking Ahead: The Use of Prospective Analysis to Improve the Quality and Safety of Care
Bibliographic record
Abstract
Patient safety and quality are paramount at Lakeridge Health, in Durham, Ontario. The use of prospective analysis has provided us with the opportunity to understand systemic issues in a hospital organization and, as such, to implement sustainable changes that are meaningful to staff and will ensure an enhanced patient experience. To complement Accreditation Canada required organizational practices and a commitment to continuous quality improvement, Lakeridge Health has recognized how an inter-professional approach, staff engagement and use of quality tools support the focus on quality and safety. The implementation of best practices (both clinical and administrative processes) has been possible as a direct result of using this approach. This article outlines three case studies representing different applications of the prospective analysis methodology: ensuring safety with endoscopy processes, minimizing risk in narcotic administration and enhancing infection control practices. In each case, the methodology of prospective analysis was used to ensure the implementation of sustainable change that spans all sites in a multi-sited health facility. This article also includes lessons learned in an effort to understand and implement this quality methodology in healthcare.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".