Surgical capacity in ophthalmology: the unmet need for sustainable solutions
Bibliographic record
Abstract
The increasing prevalence of eye diseases is placing significant pressure on surgical and procedure-based ophthalmology services worldwide. Delays in surgical care can lead to poorer patient outcomes and reduced treatment efficacy, highlighting the urgent need for healthcare systems to address these challenges. This narrative review provides a broad overview of surgical and procedure-based ophthalmology capacity constraints across five countries (UK, Germany, Australia, Singapore, and India) to identify cross-cutting, system-level challenges that transcend individual diseases or interventions, thereby informing policy and investment strategies. It examines contributing factors from patient, clinician, and healthcare system perspectives, focusing on workforce shortages, operating theatre limitations, and scheduling conflicts, while also addressing diagnostic and medical challenges affecting surgical pathways, preoperative preparation, and postoperative care. Key insights were derived from targeted literature searches and supplemented by qualitative expert interviews. The searches revealed themes including the rising prevalence of retinal diseases, workforce gaps, and the impact of capacity constraints on clinical outcomes. Expert interviews provided nuanced, qualitative perspectives from ophthalmic surgeons on local challenges and opportunities for improvement. Proven and prospective solutions were identified, including task shifting, technological innovations, and workflow optimisation. Examples such as AI-assisted diagnostics, mobile clinics, and telemedicine have successfully mitigated capacity constraints in various healthcare systems. By identifying actionable strategies, this review serves as a call-to-action to healthcare policy makers to improve surgical and procedure-based service capacity, enhance patient access to care, and ultimately optimise clinical outcomes.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".