Time to treat: A system redesign focusing on decreasing the time from suspicion of lung cancer to diagnosis in a community hospital
Bibliographic record
Abstract
17005 Background: Multiple physician visits, numerous investigations, and serial wait times often result in a lengthy process from the onset of lung cancer-related symptoms until diagnosis. An unpublished retrospective chart review from a Toronto community hospital indicated suboptimal delays for patients from onset of symptoms until the diagnosis of lung cancer. Methods: The Time to Treat Program (TTT), consisting of a streamlined referral system and a clerical facilitator to fast-track patients through a diagnostic pathway algorithm, was designed for patients with clinical or radiological suspicion of lung cancer. Data on patient visits and investigations were collected. Pre- and post-implementation data on median wait times were compared. Results: From April 2005 to December 2006 over 120 physicians referred 188 females and 226 males. For the majority of patients (95.2%), the reason for referral was chest x-ray findings suspicious for lung cancer. After TTT implementation, the median time from suspicion of lung cancer to referral for specialist consultation decreased from 19.9 days to 10 days, and the median time from such referral to the actual consultation date decreased from 16.8 days to 5.3 days. The median time from specialist consultation to CT scan decreased from 52.1 days to 4 days and the median time from CT to diagnosis decreased from 39 days to 12.4 days. Overall, the median time from suspicion of lung cancer to diagnosis decreased from 127.8 days to 30 days. For 25% of the patients in the TTT it took 13 or fewer days from suspicion of lung cancer to diagnosis, while for 5% of the patients it took 90 days or more. Half of the patients in the TTT had a diagnosis by 24 days from the time of suspicion. Of all patients in the TTT, 33% were eventually diagnosed with lung cancer. The time from suspicion to diagnosis took longer for patients who eventually had confirmed lung cancer than those who did not: 36.5 days vs. 28.7 days. Conclusions: By addressing process issues in the work-up of lung cancer, the TTT was effective in shortening the time from suspicion of lung cancer to diagnosis and reduced time intervals at each step in the process. Earlier diagnosis of lung cancer may allow increased treatment options for patients. No significant financial relationships to disclose.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".