An on-line dashboard to facilitate monitoring of provincial ICU bed occupancy in Alberta, Canada
Bibliographic record
Abstract
Intensive Care Unit (ICU) beds are among the most valuable hospital resources for which demands periodically exceed supplies. Hence monitoring and management of utilization of these resources is essential for providing an efficient and equitable service. The purpose of this article is to describe the design, development and utilization of a dashboard for the measurement of occupancy and management of capacity of a provincial network of ICUs. The dashboard utilizes the exiting hospital data sources and infrastructure to provide a timely snapshot of bed utilization as well as a historical view of unit occupancy and enables simulation scenarios for capacity planning in a dispersed geographical location. This information is used by administration for managing the scarce ICU resources and helping with standardization of admit and discharge processes to and from intensive care units in order to enhance efficiency. In our case, the existing hospital information systems proved to contain reliable data and the existing information technology infrastructure owned proper resources to be accessed to develop such valuable tool. Such dashboard presents necessary information to facilitate understanding of capacity and bed utilization and can help create a sense of community and standardization of critical care services which would eventually contribute to a more equitable and efficient health system.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".