Estimating Patient and Family Costs and CO2 Emissions for Telehealth and In-Person Health Care Appointments in British Columbia, Canada: Geospatial Mixed Methods Study
Bibliographic record
Abstract
Background Patients inevitably incur some cost for accessing health care, even in universal systems such as Canada. The COVID-19 pandemic dramatically shifted health care delivery from in-person to telehealth services, also shifting the proportion of costs offset by patients and their families by reducing the need to travel to in-person appointments. Objective This study aimed to develop a method for estimating the costs patients and their families incur and CO2 emissions attributed to travel needed for emergency department (ED) visits, hospitalizations, and physician appointments. Methods We present a method to evaluate the costs associated with in-person and telehealth care appointments from the perspective of patients, their families, and the environment. We used ED locations, road distances, and duration of appointment to account for costs paid by patients (ie, lost productivity, informal caregiving, and out-of-pocket expenses) attributed to travel to receive medical care. Costs to the environment were evaluated by calculating the amount of CO2 emitted per medical visit. Using our costs calculated per visit, we apply our method to calculate total patient costs for a simulated population over 1 year. Results Our method estimates that patients in British Columbia pay up to $300 (2023 CAD, CAD $1=US $0.86) on average to attend an in-person ED visit, depending on where they live; $166 may be attributed to lost productivity, $83 to informal caregiving, and $50 to out-of-pocket expenses. These estimates are higher than most observed cost estimates. In addition, avoiding in-person care diverts up to 13 kg of CO2 per medical visit, depending on the distance and frequency of travel to appointments. This translates to up to $0.70 in carbon costs per visit, or cumulatively $44,120 per year in British Columbia, conventionally not included in patient cost estimates. Conclusions We present a novel method for estimating patient-incurred costs and CO2 emissions from accessing health care and apply it to estimate that every year, patients in British Columbia pay upwards of 30 million dollars to access health care services, primarily for medical travel. Our method adds to the economic evaluation literature by providing a more comprehensive and context-modifiable calculation of patient costs that will allow for more informed decision-making regarding health care services.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".