Regional Process Redesign of Lung Cancer Care: A Learning Health System Pilot Project
Bibliographic record
Abstract
BACKGROUND: The Ottawa Hospital (toh) defined delay to timely lung cancer care as a system design problem. Recognizing the patient need for an integrated journey and the need for dynamic alignment of providers, toh used a learning health system (lhs) vision to redesign regional diagnostic processes. A lhs is driven by feedback utilizing operational and clinical information to drive system optimization and innovation. An essential component of a lhs is a collaborative platform that provides connectivity across silos, organizations, and professions. METHODS: To operationalize a lhs, we developed the Ottawa Health Transformation Model (ohtm) as a consensus approach that addresses process barriers, resistance to change, and conflicting priorities. A regional Community of Practice (cop) was established to engage stakeholders, and a dedicated transformation team supported process improvements and implementation. RESULTS: The project operationalized the lung cancer diagnostic pathway and optimized patient flow from referral to initiation of treatment. Twelve major processes in referral, review, diagnostics, assessment, triage, and consult were redesigned. The Ottawa Hospital now provides a diagnosis to 80% of referrals within the provincial target of 28 days. The median patient journey from referral to initial treatment decreased by 48% from 92 to 47 days. CONCLUSIONS: The initiative optimized regional integration from referral to initial treatment. Use of a lhs lens enabled the creation of a system that is standardized to best practice and open to ongoing innovation. Continued transformation initiatives across the continuum of care are needed to incorporate best practice and optimize delivery systems for regional populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".