MétaCan
Menu
← Back to cohort
Record W4295028216 · doi:10.1101/2022.09.08.22278709

Are primary care virtual visits associated with higher emergency department use? A cross-sectional analysis from Ontario, Canada

2022· preprint· en· W4295028216 on OpenAlexafffundabout
Tara Kiran, Michael Green, Rachel Strauss, C. Fangyun Wu, Maryam Daneshvarfard, Alexander Kopp, Lauren Lapointe‐Shaw, Lidija Latifovic, Eliot Frymire, Richard H. Glazier

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity Health NetworkQueen's UniversityUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchQueen's UniversityUniversity of TorontoOntario Ministry of Health and Long-Term Care
KeywordsEmergency departmentMedicineCross-sectional studyFamily medicineLogistic regressionRuralityPrimary careHealth careMedical emergencyEmergency medicineRural areaNursing

Abstract

fetched live from OpenAlex

Abstract Importance The COVID-19 pandemic has resulted in increased use of virtual care, however, few studies have looked at the association between virtual primary care visits and other healthcare use. Objective To determine whether there was an association between a high proportion of virtual visits in primary care and more emergency department visits Design A cross-sectional study, using routinely collected data Setting Ontario, Canada Participants Ontario residents alive on March 31 st 2021 and family physicians with at least 1 visit claim between February and October 2021. Exposure Family physicians stratified by the percentage of total visits that were virtual (phone or video) between February and October 2021 Main outcome(s) and measure(s) We calculated the emergency department visit rate for each stratum of family physician virtual care use. We used multivariable logistic regression models to understand the relative rate of patient emergency department use after stratifying for rurality and adjusting first for patient characteristics and then the 2019 emergency department visit rate. Results We analyzed data for 15,155 family physicians and 12,951,063 Ontarians attached to these physicians. The mean number of emergency department visits was highest among patients whose physicians provided only in-person care (470.3 ± 1918.8 per 1,000) and was lowest among physicians who provided >80 to <100% care virtually (242.0 ± 800.3 per 1,000). After adjustment for patient characteristics patients seen by physicians with >20% of visits delivered virtually had lower rates of emergency department visits compared to patients of physicians who provided >0%-20% virtually (e.g. >80 to <100% vs >0%-20% virtual visits in Big Cities, Relative Rate (RR) 0.80 [95%CI 0.76-0.83]). This trend held across all rurality strata and after adjustment for 2019 emergency department visit rates. In urban areas, there was a gradient whereby physicians providing the highest level of virtual care had the lowest emergency department visit rates. Conclusions and Relevance Physicians who provided a high proportion of care virtually did not have higher emergency department visits than those who provided the lowest levels of virtual care. Our findings refute hypotheses that emergency department use is being driven by family physicians providing more care virtually. Key points Question Do family physicians who provide more care virtually have higher emergency department visit rates among their patient panel? Findings In this cross-sectional study from Ontario, Canada, we examined data from February to October 2021 for 12,951,063 patients attached to 15,155 family doctors and found that physicians who provided a high proportion of virtual care did not have higher emergency department visits than those who provided the lowest levels of virtual care. This finding remained true after adjusting for patient characteristics. Meaning Our findings refute hypotheses that emergency department use is being driven by family physicians providing more care virtually.

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)0.002
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 score0.018
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.312
Teacher spread0.275 · 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

Citations3
Published2022
Admission routes3
Has abstractyes

Explore more

Same venuemedRxiv→Same topicTelemedicine and Telehealth Implementation→French-language works237,207→