Is it Virtually Worth It? Cost Analysis of Telehealth Monitoring for Community-based COVID-19-positive Patients
Bibliographic record
Abstract
Background: A major cost to healthcare delivery in Ontario is hospital visits. Innovations averting unnecessary hospitalizations and emergency department (ED) visits are, therefore, of paramount importance for system sustainability. A multidisciplinary clinic called the London Health Sciences Center Urgent COVID Care Clinic (LUC3) pioneered acute care for community-based COVID19-positive patients via telephone assessments paired with in-home pulse oximetry. Objectives: To identify and analyze costs and savings associated with LUC3. Methods: A retrospective observational analysis of all COVID19-positive patients referred to LUC3 between April 23, 2020 and Aug 31, 2020. We compared the cost of operating LUC3 with savings accrued from diverted or averted ambulatory visits and inpatient admissions. Two independent non-LUC3 physicians adjudicated diverted or averted hospital visits. Results: A total of 117 patients were followed for 60 days. LUC3 saved $25,495 by preventing 25 unnecessary ED visits and replacing 228 in-person appointments with telephone assessments. The net savings after accounting for LUC3 operational costs, intentional ED visits, and admissions was $11,759. Conclusions & Implications: Telemedicine clinics can be cost-beneficial when treating community-based patients with acute illnesses, provided there is ready access to physicians and use of appropriate in-home monitoring. These lessons should be applied to other acute patient populations as Canada’s healthcare system seeks resource reallocation opportunities.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".