Health Resource and Cost Savings Achieved in a Multidisciplinary Lung Cancer Clinic
Bibliographic record
Abstract
Background: Lung cancer (LC) care is resource and cost intensive. We launched a Multidisciplinary LC Clinic (MDC), where patients with a new LC diagnosis received concurrent oncology consultation, resulting in improved time to LC assessment and treatment. Here, we evaluate the impact of MDC on health resource utilization, patient and caregiver costs, and secondary patient benefits. Methods: We retrospectively analyzed patients in a rapid assessment clinic with a new LC diagnosis pre-MDC (September 2016–February 2017) and post-MDC implementation (February 2017–December 2018). Data are reported as means; unpaired t-tests and ANOVA were used to assess for significance. We also conducted a cost analysis. Resource utilization, out-of-pocket costs, procedure-related costs, and indirect costs were evaluated from the societal perspective and presented in 2019 Canadian dollars (CAD); multi-way worst/best case and threshold sensitivity analyses were conducted. Results: We reviewed 428 patients (78 traditional model, 350 MDC). Patients in the MDC model required significantly fewer oncology visits from LC diagnosis to first LC treatment (1.62 vs. 2.68, p < 0.001), which was significant for patients with stage 1, 3, and 4 disease. Compared with the traditional model, there was no change in mean biopsies/patient (1.32 traditional vs. 1.17 MDC, p = 0.18) or staging investigations/patient (2.24 traditional vs. 2.02 MDC, p = 0.20). Post-MDC, there was an increase in invasive mediastinal staging for patients with stage 2/3 LC (15.0% vs. 60.0%, p < 0.001). Over 22 months, MDC resulted in savings of CAD 48,389 including CAD 24,167 CAD in direct patient out-of-pocket expenses. For the threshold analyses, MDC was estimated to cost CAD 25,708 per quality-adjusted life year (QALY), considered to be below current willingness to pay thresholds (at CAD 80,000 per QALY). MDC also facilitated oncology assessment for 29 non-LC patients. Conclusions: An MDC led to a reduction in patient visits and direct patient and caregiver costs.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".