Early Detection of High-Risk Claims at the Workers' Compensation Board of British Columbia
Bibliographic record
Abstract
We developed a combined decision-analysis and logistic-regression approach for identifying high-risk claims at the Workers' Compensation Board of British Columbia (WCB). The early detection of such claims and subsequent intervention is likely to reduce their eventual cost and to speed up worker rehabilitation. High-risk claims are extremely costly to the WCB; for the approximately 321,000 short-term disability claims with injury dates between 1989 and 1992, high-risk claims accounted for $1.2 billion (64 percent) of the total payment of $1.8 billion, even though they constituted only 4.2 percent of the claims. We developed separate logistic regression models for each injury type. We found that the age of worker and number of workdays lost were predictive of high-risk status. We used decision analysis to develop a classification rule that has high out-of-sample predictive power. The WCB has incorporated these results in a claims-profiling scorecard, which identifies claims needing early intervention. We estimate that our method saves the WCB $4.7 million annually.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".