Examination of Health System Resources and Costs Associated With Transitioning Cancer Survivors to Primary Care: A Propensity-Score–Matched Cohort Study
Bibliographic record
Abstract
BACKGROUND:: Transitioning low-risk cancer survivors back to their primary care provider (PCP) has been shown to be safe but the effect on health system resources and costs has not been examined. METHODS:: A Well Follow-Up Care Initiative (WFCI) was implemented in the publicly funded health system. Low-risk breast cancer (BC) survivors in the WFCI intervention group were transitioned from oncologist-led cancer clinics to PCPs. We compared health system costs ($2,014 in Canadian dollars) and resource utilization in this intervention group with that in propensity-score-matched nontransitioned BC survivors (ie, controls) diagnosed in the same year, with similar disease profile and patient characteristics using publicly funded administrative databases. RESULTS:: A total of 2,324 BC survivors from the WFCI intervention group were 1:1 matched to controls and observed for 25 months. Compared with controls, survivors in the intervention group incurred a similar number of PCP visits (6.9 v 7.5) and fewer oncologist visits (0.3 v 1.2) per person-year. Fewer survivors in the intervention group (20.1%) were hospitalized than in the control group (24.4%). There were no differences in emergency visits. More survivors in the intervention group had mammograms (82.6% v 73.1%), but other diagnostic tests were less frequent. There was a 39.3% reduction in overall mean annual costs ($6,575 v $10,832) and a 22.1% reduction in overall median annual costs ($2,261 v $2,903). Overall survival in the intervention group was not worse than controls. CONCLUSION:: Transitioning low-risk BC survivors to PCPs was associated with lower health system resource use and a lower annual cost per patient than matched controls. The WFCI model represents a reasonable approach at the population level to delivering quality care for low-risk BC survivors that seems to be cost effective.
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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 | 0.004 | 0.000 |
| 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 | 0.000 |
| 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".