Improving Access to Care, Patient Costs, and Environmental Impact Through a Community Outreach Lung Cancer Rapid Assessment Clinic
Bibliographic record
Abstract
PURPOSE In Southeastern Ontario, increased patient distance from the regional lung cancer diagnostic assessment program (LDAP) is associated with a lower likelihood of patient care via LDAP while receiving care via LDAP is associated with improved survival. We implemented an LDAP outreach clinic to provide specialist assessment for patients with suspected lung cancer at a regional community hospital and assessed the impact on timeliness and accessibility of care. MATERIALS AND METHODS The Kingston Health Sciences Centre LDAP team engaged with community hospital partners to develop and launch the LDAP outreach clinic. We performed a retrospective chart review of LDAP patients (N = 1,070) before (August-November 2021; n = 234) and after implementation of the outreach clinic (November 2021-October 2022; n = 836). Descriptive data are reported as No. (%). Unpaired t tests and statistical process control charts assess for significance. A cost analysis of out-of-pocket patient costs related to travel and parking is presented in 2022 Canadian dollars (CAD). RESULTS Compared with a 3-month matched time period before (August-October 2021) and after outreach clinic (August-October 2022), the mean time from referral to assessment and time from referral to diagnosis decreased from 20.3 to 14.4 days ( P = .0019) and 40.0 to 28.9 days ( P = .0007), respectively. Over 12 months, the total patient travel was reduced by 8,856 km, which combined with parking cost-savings, resulted in patient out-of-pocket savings of CAD $5,755.60 (CAD $47.60/patient). Accounting for physician travel, the total travel saved was 5,688 km, corresponding to reduced CO 2 emissions by 1.9 tCO 2 . CONCLUSION Implementation of a lung cancer outreach clinic led to improved timeliness of care, patient cost-savings, and reduced carbon footprint while serving patients in their community.
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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 | 0.001 |
| 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".