Implementing teleophthalmology services to improve cost-effectiveness of the national eye care system
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Optometrist-assisted and teleophthalmology-enabled referral pathway (OTRP) for community optometry referrals has the potential to improve the capacity and efficiency of eye care delivery systems through risk stratification and limiting the number of improved referrals. This study investigates the expected future costs and benefits of implementing OTRP under various possible organizational set-ups relevant to a Danish context. METHODS: A decision-analytic model (decision tree) with a one-year time horizon was constructed to portray alternative future patient referral pathways for people examined in optometry stores for suspected ocular posterior segment eye disease. The main outcomes were total healthcare costs per patient, average waiting time from eye examination in store until the start of treatment or end of referral pathway, and quality-adjusted life-years (QALY) gained. The economic evaluation compares the general ophthalmologist referral pathway (GO-RP) with a potential reimbursement model for the optometrist-assisted teleophthalmology referral pathways (R-OTRP) and a procurement model for the optometrist-assisted teleophthalmology referral pathways (P-OTRP). RESULTS: The cost per individual with suspected ocular posterior segment eye disease was estimated to be £116 for GO-RP and £75 and £94 for P-OTRP and R-OTRP respectively. The average waiting time for diagnosis or end of referral pathway was 25 weeks for GO-RP and 5.8 and 5.7 for P-OTPR and R-OTPR respectively. QALY gain was 0.15 for P-OTRP/R-OTRP compared to 0.06 for GO-RP. CONCLUSION: OTRP is effective in reducing unnecessary referrals and waiting times, increasing patients' HRQoL, and decreasing the costs of diagnosing individuals with suspected ocular posterior segment eye disease.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".