Cost Analysis of a Patient Portal Used to Remotely Monitor COVID-19 Patients in Quebec
Bibliographic record
Abstract
BACKGROUND: Telemonitoring for COVID-19 has gained much attention due to its potential in reducing morbidity, healthcare utilization, and costs. However, its benefit with regard to economic outcomes has yet to be clearly demonstrated. OBJECTIVE: To analyze the costs associated with the use of the Opal portal to monitor COVID-19 patients during their 14-day confinement in Quebec and compare them to those of non-users of any home telemonitoring technology. METHODS: A cost analysis was conducted through a cross-sectional study between COVID-19 patients who used (PU) the Opal platform during their 14-day confinement at home and those who did not use (PNU) any home remote monitoring technology. Data was collected between June 2021 to April 2022. An individual interview with each participant using an adapted questionnaire was conducted by telephone or online using a teleconferencing platform. A micro-costing approach was undertaken using a dual patient and Quebec's health-care system perspective. RESULTS: 27 telemonitoring participants, 29 non-users, 8 clinicians, and 4 managers were included. Telemonitoring reduced the average total costs incurred by PU by 82% ($537.3CAD) between PU ($117.2CAD) and PNU ($654.5CAD). Telemonitoring enrollees used healthcare less intensely with fewer emergency room visits (1 PU compared to 6 PNU), which translated to an average savings of $253.3CAD per patient. CONCLUSION: This is the first study to demonstrate that telemonitoring through the Opal platform is a viable strategy to reduce healthcare costs and utilization for patients and the healthcare system. The evidence provides strong support for introducing telemonitoring as a component of case management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".