A device category economic model of electrosurgery technologies across procedure types: a U.S. hospital budget impact analysis
Bibliographic record
Abstract
AIMS: The electrosurgical technology category is used widely, with a diverse spectrum of devices designed for different surgical needs. Historically, hospitals are supplied with electrosurgical devices from several manufacturers, and those devices are often evaluated separately; it may be more efficient to evaluate the category holistically. This study assessed the health economic impact of adopting an electrosurgical device-category from a single manufacturer. METHODS: A budget impact model was developed from a U.S. hospital perspective. The uptake of electrosurgical devices from EES (Ethicon Electrosurgery), including ultrasonic, advanced bipolar, smoke evacuators, and reusable dispersive electrodes were compared with similar MED (Medical Energy Devices) from multiple manufacturers. It was assumed that an average hospital performed 10,000 annual procedures 80% of which involved electrosurgery. Current utilization assumed 100% MED use, including advanced energy, conventional smoke mitigation options (e.g. ventilation, masks), and single-use disposable dispersive electrode devices. Future utilization assumed 100% EES use, including advanced energy devices, smoke evacuators (i.e. 80% uptake), and reusable dispersive electrodes. Surgical specialties included colorectal, bariatric, gynecology, thoracic and general surgery. Systematic reviews, network meta-analyses, and meta-regressions informed operating room (OR) time, hospital stay, and transfusion model inputs. Costs were assigned to model parameters, and price parity was assumed for advanced energy devices. The costs of disposables for dispersive electrodes and smoke-evacuators were included. RESULTS: The base-case analysis, which assessed the adoption of EES instead of MED for an average U.S. hospital predicted an annual savings of $824,760 ($101 per procedure). Savings were attributable to associated reductions with EES in OR time, days of hospital stay, and volume of disposable electrodes. Sensitivity analyses were consistent with these base-case findings. CONCLUSIONS: Category-wide adoption of electrosurgical devices from a single manufacturer demonstrated economic advantages compared with disaggregated product uptake. Future research should focus on informing comparisons of innovative electrosurgical devices.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".