Economic impact of the Swiss Diagnosis-Related Group system on acute neurorehabilitation
Bibliographic record
Abstract
Objective: To assess the economic impact of introducing the Swiss Diagnosis-Related Group (DRG)-financing system on the Acute Neurorehabilitation Unit (ANRU) of a University hospital in 2012 and to discuss the implications in 2020.Methods: A retrospective study using monocentric patient cohort and collecting anonymized data of consecutive patients admitted to the ANRU in 2012 and 2013. The characteristics, DRG A43Z, costs and revenues were retrieved from the hospital accounting system and allowed a comparison between the 2012 and 2013 groups of patients.Results: Forty-seven patients were included over the assessment period. In 2012, of the 23 patients admitted, 20 were coded A43Z, while in 2013, out of the 24 admissions, only eight had that specific code (p < .01). The average length of stay (LOS) increased from 45.5 days in 2012 to 49.5 days in 2013. Similarly, the average cost per patient increased by Swiss Franc (CHF) 19,994 over the two years, from CHF 183,634 in 2012 to CHF 194,629 in 2013. Finally, the average reimbursement per patient diminished by CHF 11,392, from CHF 193,153 in 2012 to CHF 181,760 in 2013.Conclusions: The negative impact on the cost–revenue balance is linked to both the increased cost of a longer stay and the decreased revenue due to less patients being coded A43Z. This study highlights the difficulties to justify funding of the complex care needed and to properly reflect patient burden in medico-administrative documents. Certainly, there is a need for a concerted effort to identify the services and resources needed within the DRG-system to guarantee the optimal management of acute neurorehabilitation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".