Analysis of Acuity Trends Using Resource Intensity Weights Via the CIHI Portal
Bibliographic record
Abstract
One key to revolutionizing health care with informatics is the ability of decision-makers to access and analyze relevant data in a timely and efficient manner. Inspired by the demand for timely access to hospitalization data in Canada, CIHI Portal is an innovative web-based analytical tool which combines leading technology and data for decision support analysis. Hospitals, regional health authorities and ministries of health can use CIHI Portal to access comparable, pan-Canadian healthcare data for health data analysis, collaboration and dissemination. The goal of CIHI Portal is to support health care decision-makers in their local and regional health care planning and to answer service delivery questions.The Capital Health region in Alberta used Resource Intensity Weights (RIW) to investigate claims that patients within their region were getting sicker over the past few years and that additional resources would be required in the future. Using the CIHI Portal, Capital Health conducted an analysis on historical trends in the average RIWs Average Resource Intensity Weight is calculated as the total Resource Intensity Weight (RIW) divided by the total number of inpatient separations. and found that although typical patients were not using a greater amount of resources, there was definitely an increase in the amount of resources consumed by atypical patients. Information contained in the analysis influenced budgeting, fund reallocation and health care planning. CIHI Portal has proven to be a reliable tool for data access, information sharing and knowledge exchange. It has enhanced decision support services within the Capital Health region.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".