Transitioning Initial Success into Sustainable Results: The Future of the WTIS
Bibliographic record
Abstract
Getting a new concept or project up and running is never an insignificant undertaking. In many cases, however, the successful completion of a project signals the start of the "real work" in which the greater challenge is in turning that initial success and investment into results that can be sustained over the long-term. This was the challenge facing Cancer Care Ontario (CCO) once they developed and deployed the Wait Time Information System (WTIS) on behalf of the Ontario Ministry of Health and Long-Term Care (MOHLTC). With the launch of the WTIS, the government, Local Health Integration Networks (LHINs), hospitals and patients had--for the first time--standardized, near real-time data to make more informed healthcare decisions and better manage access to critical health services. But much more work was ahead. The next step was to begin leveraging the technology and the wealth of data it provided to help drive significant performance improvements within the overall health system. The time had come to shift gears from an IT deployment project to a sustainable operations and information management program that could continue to provide value for the province. This article looks at the future of the WTIS and describes the journey CCO has taken to establish a permanent Wait Time Information Program. These concepts will be of interest to leaders of or participants in information management/information technology (IM/IT) projects looking for ideas on how to smoothly transition a successful project into a sustainable operational program.
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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 | 0.056 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".