Cost savings associated with improving appropriate and reducing inappropriate preventive care: cost-consequences analysis
Bibliographic record
Abstract
BACKGROUND: Outreach facilitation has been proven successful in improving the adoption of clinical preventive care guidelines in primary care practice. The net costs and savings of delivering such an intensive intervention need to be understood. We wanted to estimate the proportion of a facilitation intervention cost that is offset and the potential for savings by reducing inappropriate screening tests and increasing appropriate screening tests in 22 intervention primary care practices affecting a population of 90,283 patients. METHODS: A cost-consequences analysis of one successful outreach facilitation intervention was done, taking into account the estimated cost savings to the health system of reducing five inappropriate tests and increasing seven appropriate tests. Multiple data sources were used to calculate costs and cost savings to the government. The cost of the intervention and costs of performing appropriate testing were calculated. Costs averted were calculated by multiplying the number of tests not performed as a result of the intervention. Further downstream cost savings were determined by calculating the direct costs associated with the number of false positive test follow-ups avoided. Treatment costs averted as a result of increasing appropriate testing were similarly calculated. RESULTS: The total cost of the intervention over 12 months was $238,388 and the cost of increasing the delivery of appropriate care was $192,912 for a total cost of $431,300. The savings from reduction in inappropriate testing were $148,568 and from avoiding treatment costs as a result of appropriate testing were $455,464 for a total savings of $604,032. On a yearly basis the net cost saving to the government is $191,733 per year (2003 Can dollars) equating to $3,687 per physician or $63,911 per facilitator, an estimated return on intervention investment and delivery of appropriate preventive care of 40%. CONCLUSION: Outreach facilitation is more expensive but more effective than other attempts to modify primary care practice and all of its costs can be offset through the reduction of inappropriate testing and increasing appropriate testing. Our calculations are based on conservative assumptions. The potential for savings is likely considerably higher.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".