Defining the Cost of Arthroscopic Rotator Cuff Repair
Bibliographic record
Abstract
BACKGROUND: Rotator cuff repair (RCR) is a frequently performed outpatient orthopaedic surgery, with substantial financial implications for health-care systems. Time-driven activity-based costing (TDABC) is a method for nuanced cost analysis and is a valuable tool for strategic health-care decision-making. The aim of this study was to apply the TDABC methodology to RCR procedures to identify specific avenues to optimize cost-efficiency within the health-care system in 2 critical areas: (1) the reduction of variability in the episode duration, and (2) the standardization of suture anchor acquisition costs. METHODS: Using a multicenter, retrospective design, this study incorporates data from all patients who underwent an RCR surgical procedure at 1 of 4 academic tertiary health systems across the United States. Data were extracted from Avant-Garde Health's Care Measurement platform and were analyzed utilizing TDABC methodology. Cost analysis was performed using 2 primary metrics: the opportunity costs arising from a possible reduction in episode duration variability, and the potential monetary savings achievable through the standardization of suture anchor costs. RESULTS: In this study, 921 RCR cases performed at 4 institutions had a mean episode duration cost of $4,094 ± $1,850. There was a significant threefold cost variability between the 10th percentile ($2,282) and the 90th percentile ($6,833) (p < 0.01). The mean episode duration was registered at 7.1 hours. The largest variability in the episode duration was time spent in the post-acute care unit and the ward after the surgical procedure. By reducing the episode duration variability, it was estimated that up to 640 care-hours could be saved annually at a single hospital. Likewise, standardizing suture anchor acquisition costs could generate direct savings totaling $217,440 across the hospitals. CONCLUSIONS: This multicenter study offers valuable insights into RCR cost as a function of care pathways and suture anchor cost. It outlines avenues for achieving cost-savings and operational efficiency. These findings can serve as a foundational basis for developing health-economics models. LEVEL OF EVIDENCE: Economic and Decision Analysis Level III. See Instructions for Authors for a complete description of levels of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".