Lung cancer diagnosis transformation: Aligning the people, processes, and technology sides of the learning system.
Bibliographic record
Abstract
50 Background: The Ottawa Hospital, serving a population of 1.2 million across a large geographic area, redesigned its regional lung cancer diagnosis and patient intake process using a systems approach. Although having a strong record of excellent coordinated care, recent challenges had created strains on delivery metrics. Prior to the implementation of the project (October 2014), it took patients a range of 15 – 225 days to get from a referral with a suspicion of lung cancer to initial treatment, with a 90 th percentile of 117 days. Methods: An innovative care platform was developed to align the people, processes and technology sides of the regional lung cancer care. It formed a basis for a “learning” system mobilizing the wider community for transformational change. We redesigned 12 main business processes facilitating accurate and timely diagnoses using the Communities of Practice approach, the Lean and Constraints methodology and the IBM business process management technology tools. In a series of rapid learning cycles, 270 technology-enabled actions were logged and resolved to support 57 process changes in workflow. The main patient value outcome was defined as improvement in the monthly proportion of patients receiving initial treatment within provincial targets and a reduction of total wait time. Results: In 14 months, the TOH Lung Cancer Transformation program yielded substantial improvement across all levels of diagnostic process including a 48% reduction in the cumulative wait times from referral to initial treatment (surgical, systemic, radiation), increased patient satisfaction with care coordination, implementation of guideline standards, efficiency gains from more intensive use of hospital resources, and sustained engagement of over 100 regional lung cancer care providers in collaborative learning and knowledge sharing across disciplinary and organizational boundaries. Conclusions: A systems approach can improve the hospital capacity to facilitate and support inter-professional and intra-professional teamwork necessary for the diagnostic process transformation.
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 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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".