PB5 Establishment of a provincial data repository to facilitate reporting performance measurements for Alberta Emergency Departments
Bibliographic record
Abstract
Objectives and Background The ability to accurately define and measure Emergency Department (ED) patient utilisation and flow parameters, as well as metrics related to patient safety and quality of care are essential to the operations and accountability of emergency patient care delivery. Alberta Health Services (AHS) is responsible for the delivery of health services to 3.7 million people in Alberta (AB), Canada, with total provincial ED visits exceeding 1.9 million/year. The Data Integration, Measurement and Reporting Department of AHS, in collaboration with emergency clinical leadership, has undertaken the development for the AHS Data Repository for Reporting (AHSDRR). This database will provide unique functionality. It can be interrogated by a wide range of stakeholders to yield quality and performance indicator reports with a variety of adjustable filtering parameters, such as patient demographics, institution and region. Methods The AHSDRR architecture integrates clinical and administrative data from multiple sources. Data sources will include 11 urban teaching EDs, one freestanding ED, two Urgent Care centers, and four regional EDs utilising ED Information Systems. A comprehensive standardised ED data set from chart abstraction will be included in the AHSDRR for all provincial EDs. ED patient experience survey data and vital statistics data will be incorporated. Results With successful development and testing, AHSDRR Emergency Release 1 became operational on September 15th, 2011. Conclusions The AHSDRR represents an innovative opportunity to employ multiple data resources to describe emergency care delivery in Alberta. This system will facilitate standardised reporting of ED performance and quality information in formats compliant with national and international standards. With additional clinical information sources, AHSDRR future applications have considerable growth potential.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".