Increased Virtual Visits to Physicians During the COVID-19 Pandemic and Estimated Impact on Physician Compensation: The Case of Lung and Colorectal Cancers, Chronic Obstructive Pulmonary Diseases, and Heart Failure in Alberta, Canada
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic started in Alberta in March 2020 and significantly increased telehealth service use and provision reducing the risk of virus transmission. We examined the change in the number and proportion of virtual visits by physician specialty and condition (chronic obstructive pulmonary diseases [COPD], heart failure [HF], colorectal and lung cancers), as well as associated changes in physician compensation. Methods: A population-based design was used to analyze all processed physician claims comparing the number and proportion of virtual visits and associated physician billings relative to in-person between pre- (2019/2020) and intra-pandemic (2020/2021). Physician compensations were the claim amounts paid by the health insurance. Results: Pre-pandemic (intra-), there were 8,981 (8,897) lung cancer, 9,245 (9,029) colorectal, 37,558 (36,292) HF, and 68,270 (52,308) COPD patients. Each patient had totally 2.3–4.7 (of which 0.4–0.6% were virtual) general practitioner (GP) visits and 0.9–2.3 (0.2–0.7% were virtual) specialist visits per year pre-pandemic. The average number and proportion of per-patient virtual visits to GPs and specialists grew significantly pre- to intra-pandemic by 2,138–4,567%, and 2,201–7,104%, respectively. Given the lower fees of virtual compared with in-person visits, the reduction in physician compensation associated with the increased use of virtual care was estimated at $3.85 million, with $2.44 million attributed to specialist and $1.41 million to GP. Discussion: Utilization of telehealth increased significantly, while the physician billings per patient and physician compensation declined early in the pandemic in Alberta for the four chronic diseases considered. This study forms the basis for future study in understanding the impact of virtual care, now part of the fabric of health care delivery, on quality of care and patient safety, overall health service utilization (such as diagnostic imaging and other investigations), as well as economic impacts to patients, health care systems, and society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".