Economic Evaluation of Telerobotic Ultrasound Technology to Remotely Provide Ultrasound Services in Rural and Remote Communities
Bibliographic record
Abstract
INTRODUCTION: Telerobotic ultrasound technology allows radiologists and sonographers to remotely provide ultrasound services in underserved areas. This study aimed to compare costs associated with using telerobotic ultrasound to provide ultrasound services in rural and remote communities to costs associated with alternate models. METHODS: A cost-minimization approach was used to compare four ultrasound service delivery models: telerobotic ultrasound (Model 1), telerobotic ultrasound and an itinerant sonographer (Model 2), itinerant sonographer without telerobotic ultrasound (Model 3), and travel to another community for all exams (Model 4). In Models 1-3, travel was assumed when exams could not be successfully performed telerobotically or by an itinerant sonographer. A publicly funded healthcare payer perspective was used for the reference case and a societal perspective was used for a secondary non-reference case. Costs were based on the literature and experience using telerobotic ultrasound in Saskatchewan, Canada. Costs were expressed in 2020 Canadian dollars. RESULTS: Average cost per ultrasound exam was $342, $323, $368, and $478 for Models 1, 2, 3, and 4, respectively, from a publicly funded healthcare payer perspective, and $461, $355, $447, and $849, respectively, from a societal perspective. In one-way sensitivity analyses, Model 2 was the lowest cost from a payer perspective for communities with population >2075 people, distance >350 km from the nearest ultrasound facility, or >47% of the population eligible for publicly funded medical transportation. CONCLUSION: Health systems may wish to consider solutions such as telerobotic ultrasound and itinerant sonographers to reduce healthcare costs and improve access to ultrasound in rural and remote communities.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".