Financial model for a transitional pain service at a large tertiary academic center in the USA
Bibliographic record
Abstract
Approximately 1 in 10 patients undergoing surgery is considered at high risk for poor pain and opioid-related outcomes due to chronic pain or persistent opioid use prior to surgery, leading to increased hospital lengths of stay, emergency department visits, hospital readmissions, and worse long-term outcomes. Multidisciplinary transitional pain services (TPSs) have been shown to effectively identify and optimize high-risk patients before surgery, leading to a reduction in healthcare utilization. We conducted a series of semistructured interviews, a literature search, and a financial analysis to develop a reproducible business case for establishing a TPS. These interviews involved discussions with clinicians and administrators at Michigan Medicine, as well as leaders of TPS initiatives at peer institutions across the USA and Canada. The aim was to understand possible operational structures and potential sources of revenue and cost savings that needed inclusion in our model. Subsequently, the authors developed a modifiable financial modeling tool, which is freely available for download and adaptable to any healthcare institution. The model suggests that the primary source of cost savings can be attributed to a reduction in length of stay. Furthermore, several operational options exist for incorporating a TPS that performs at breakeven or positive net profit. This tool and these findings are important for informing health systems of operational and financial considerations when implementing a TPS program. Future research should evaluate this financial tool's reproducibility in community health system contexts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".