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Record W4410144394 · doi:10.1101/2025.05.06.25327128

Impact of billing policy changes on telehealth use in Ontario: a population-based repeated cross-sectional study

2025· preprint· en· W4410144394 on OpenAlexaffabout
Vess Stamenova, Cherry Chu, Jiming Fang, Onil Bhattacharyya, R. Sacha Bhatia, Mina Tadrous

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of TorontoWomen's College HospitalToronto Metropolitan University
Fundersnot available
KeywordsTelehealthCross-sectional studyPopulationMedicineBusinessEnvironmental healthTelemedicineHealth careEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Abstract As telehealth is being integrated into a regularly functioning system, policy makers have been adding some restrictions related to its use (e.g. modalities and pre-existing in-person relationship rules). We explored how the new policies impacted the levels of use across telehealth modalities and if the impact varied across sociodemographic and chronic condition groups of patients. This is a population-based repeated cross-sectional study examining all outpatient visits in Ontario, Canada on a weekly basis from the week of January 1st, 2018 until the week of December 25 th , 2023. We used linked health administrative databases of health services provided to all Ontario residents who are insured through the Ontario Health Insurance Plan (OHIP). We examined the total number of visits and the rates of in-person and telehealth visits per 1000 persons per week. Across Ontario, there were 115 046 536 telehealth visits during the study time period (26.4% of all ambulatory care). There was a 6.7% reduction in telehealth and a 10% reduction in the number of physicians using telehealth at the beginning of December 2022 when the new policies were introduced. This was in the absence of a reduction of total ambulatory visits. The impact varied across medical specialties, patient age groups, rurality and chronic conditions, but seemingly not across sex or income quintiles. The use of video increased slightly over the study period with 1 in 4 telehealth visits occurring over video. While the policy changes led to an overall reduction in telehealth use, the total ambulatory visits did not change, suggesting a shift of care from virtual to in-person. The adoption of video increased, but future studies should focus on exploring whether there are clear benefits of using video over telephone, as certain groups of patients may be impacted more than others. Author Summary As healthcare systems returned to normal functioning after the pandemic, rules around the use of telehealth (use of telephone and video to provide care) changed. For example, in Ontario, Canada, physicians were paid on par for video visits as in-person visits, but telephone visits were paid at 85% of the rate. In addition, the government introduced requirements related to whether a patient has been seen in-person by a physician within the last two years prior to a telehealth visit. Our study explored the impact of these changes using physician billing data. Overall, there was a 6.7% reduction in telehealth and a 10% reduction in the number of physicians using telehealth when the new policies were introduced in Dec, 2022. The impact varied across medical specialties, patient age groups, rurality and chronic conditions, but seemingly not across sex or income quintiles. Overall outpatient visits were not impacted, suggesting that care shifted back to in-person. The majority of telehealth still occurred over telephone, despite a slight increase in the use of video after the policies were introduced.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.109
GPT teacher head0.441
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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