Economic, Environmental, and Social Value of Virtual Care in Otolaryngology: Sustainability in Quality Improvement Framework
Bibliographic record
Abstract
OBJECTIVE: Apply the Sustainability in Quality Improvement framework to virtual care for Otolaryngology-Head and Neck Surgery (OHNS) patients to understand the economic, environmental, and social impacts. METHODS: This project consisted of retrospective analysis of anonymized data from all appointments that took place in three academic ambulatory OHNS clinics (pediatrics, head and neck, and otology/neurotology) from fiscal years of 2021 to 2023. Data were obtained from our institution's Virtual Care Dashboard. The following metrics were calculated: travel costs avoided with virtual appointments (economic value), fuel and carbon emissions avoided with virtual appointments (environmental value), and differences in Ontario Marginalization (ON-Marg) Index scores between patients seen virtually versus in-person (social value). RESULTS: A total of 41,343 visits occurred over the 2-year period (18.1% virtual). Nearly all virtual visits were by telephone (99.6%). The average cost savings per virtual care visit was $87.50, and total cost savings across all 3 clinics was $640,300. Total environmental savings were 82,500 L of fuel and 246.6 metric tons of carbon emissions. There were no statistical differences in monthly average marginalization (ON-Marg) indices in patients seen virtually compared to in-person. DISCUSSION: Virtual care demonstrated financial and environmental savings for OHNS patients that can accumulate over multiple appointments. No difference in ON-Marg indices between patients assessed virtually versus in-person suggests that virtual care was accessible for patients regardless of social background. IMPLICATIONS FOR PRACTICE: Our data suggests that virtual care may be a viable complement for delivering OHNS care that leads to fiscal and environmental savings for patients and ensures equitable access to care.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".