Financial impact of spinal cord stimulation on the healthcare budget: a comparative analysis of costs in Canada and the United States
Bibliographic record
Abstract
OBJECT: Many institutions with spinal cord stimulation (SCS) programs fail to realize that besides the initial implantation cost, budgetary allocation must be made to address annual maintenance costs as well as complications as they arise. Complications remain the major contributing factor to the overall expense of SCS. The authors present a formula that, when applied, provides a realistic representation of the actual costs necessary to implant and maintain SCS systems in Canada and the US. METHODS: The authors performed a retrospective analysis of 197 cases involving SCS (161 implanted and 36 failed trial stimulations) between 1995 and 2006. The cost of patient workup, initial implantation, annual maintenance, and resources necessary to resolve complications were assessed for each case and a unit cost applied. The total cost allocated for each case was determined by summing across healthcare resource headings. Using the same parameters, the unit cost was calculated in both Canadian (CAD) and US dollars (USD) at 2007 prices. RESULTS: The cost of implanting a SCS system in Canada is $21,595 (CAD), in US Medicare $32,882 (USD), and in US Blue Cross Blue Shield (BCBS) $57,896 (USD). The annual maintenance cost of an uncomplicated case in Canada is $3539 (CAD), in US Medicare $5071 (USD), and in BCBS $7277 (USD). The mean cost of a complication was $5191 in Canada (range $136-18,837 [CAD]). In comparison, in the US the figures were $9649 (range $381-28,495) for Medicare and $21,390 (range $573-54,547) for BCBS (both USD). Using these calculations a formula was derived as follows: the annual maintenance cost (a) was added to the average annual cost per complication per patient implanted (b); the sum was then divided by the implantation cost (c); and the result was multiplied by 100 to obtain a percentage (a + b / c x 100). To make this budgetary cap universally applicable, the results from the application of the formula were averaged, resulting in an 18% premium. CONCLUSIONS: For budgeting purposes the institution should first calculate the initial implantation costs that then can be "grossed up" by 18% per annum. This amount of 18% should be in addition to the implantation costs for the individual institution for new patients, as well as for each actively managed patient. This resulting amount will cover the costs associated with annual maintenance and complications for every actively managed patient. As the initial cost of implantation in any country reflects their current economics, the formula provided will be applicable to all implanters and policy makers alike.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".