Successful Implementation of Femtosecond Laser-Assisted Cataract Surgery: A Real-World Economic Analysis
Bibliographic record
Abstract
PURPOSE: To calculate the minimum number of Femtosecond laser-assisted cataract surgery (FLACS) procedures required per month to pay off the fixed investment cost over 5 years to achieve break-even. SETTING: A rural ophthalmology practice located in the mid-West United States. DESIGN: An economic analysis, based on real-world, retrospectively collected data over 12 months, from an ambulatory surgical care perspective. METHODS: laser (Alcon Vision LLC., Fort Worth, TX). The incremental cost of FLACS, cases needed to break-even, return on investment (ROI), patient education, and marketing efforts were assessed. The financial analysis considered cataract volume, conversion rates, fixed (eg, principal) and variable (eg, supplies) costs, and revenue in the first 12 months. RESULTS: The clinic performed 2717 cataract surgeries in the 12-month period, with 1304 (48%) of patients converting to FLACS. Of FLACS procedures, 613 (47%) selected an advanced-technology intraocular lens (AT-IOL; eg, toric or lifestyle IOL), and the remaining patients selected a monofocal IOL with laser astigmatism correction. FLACS increased AT-IOL use by 113 procedures (23%) compared to volumes in the year prior to FLACS. Overall, FLACS was predicted to be profitable, with only 13 cases required per month to break even in 5 years. If both facility and physician fees are considered revenue, only eight cases per month are required to break-even in 5 years. CONCLUSION: The practice experienced a greater-than-anticipated conversion to FLACS and increased selection of AT-IOLs, well above the break-even volume required, contributing to a rapid return on their investment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.008 | 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".