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Virtual Care and Emergency Department Use During the COVID-19 Pandemic Among Patients of Family Physicians in Ontario, Canada

2023· article· en· W4367311066 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

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity Health NetworkQueen's UniversityPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineEmergency departmentRuralityFamily medicinePrimary carePandemicCoronavirus disease 2019 (COVID-19)PopulationCross-sectional studyDemographyEmergency medicineEnvironmental healthNursingRural areaInternal medicineDisease

Abstract

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Importance: The COVID-19 pandemic has played a role in increased use of virtual care in primary care. However, few studies have examined the association between virtual primary care visits and other health care use. Objective: To evaluate the association between the percentage of virtual visits in primary care and the rate of emergency department (ED) visits. Design, Setting, and Participants: This cross-sectional study used routinely collected administrative data and was conducted in Ontario, Canada. The sample comprised family physicians with at least 1 primary care visit claim between February 1 and October 31, 2021, and permanent Ontario residents who were alive as of March 31, 2021. All residents were assigned to physicians according to enrollment and billing data. Exposure: Family physicians' virtual visit rate was the exposure. Physicians were stratified by the percentage of total visits that they delivered virtually (via telephone or video) during the study period (0% [100% in person], >0%-20%, >20%-40%, >40%-60%, >60%-80%, >80% to <100%, or 100%). Main Outcomes and Measures: Population-level ED visit rate was calculated for each stratum of virtual care use. Multivariable regression models were used to understand the relative rate of patient ED use after adjusting for rurality of practice, patient characteristics, and 2019 ED visit rates. Results: Data were analyzed for a total of 13 820 family physicians (7114 males [51.5%]; mean [SD] age, 50 [13.1] years) with 12 951 063 patients (6 714 150 females [51.8%]; mean [SD] age, 42.6 [22.9] years) who were attached to these physicians. Most physicians provided between 40% and 80% of care virtually. A higher percentage of the physicians who provided more than 80% of care virtually were 65 years or older, female individuals, and practiced in big cities. Patient comorbidity and morbidity were similar across strata of virtual care use. The mean (SD) number of ED visits was highest among patients whose physicians provided only in-person care (470.3 [1918.8] per 1000 patients) and was lowest among patients of physicians who provided more than 80% to less than 100% of care virtually (242.0 [800.3] per 1000 patients). After adjustment for patient characteristics, patients of physicians with more than 20% of visits delivered virtually had lower rates of ED visits compared with patients of physicians who provided more than 0% to 20% of care virtually (eg, >80% to <100% vs >0%-20% virtual visits in big cities: relative rate, 0.77%; 95% CI, 0.74%-0.81%). This pattern was unchanged across all rurality of practice strata and after adjustment for 2019 ED visit rates. In urban areas, there was a gradient whereby patients of physicians providing the highest level of virtual care had the lowest ED visit rates. Conclusions and Relevance: Findings of this study show that patients of physicians who provided a higher percentage of virtual care did not have higher ED visit rates compared with patients of physicians who provided the lowest levels of virtual care. The findings refute the hypothesis that family physicians providing more care virtually during the pandemic resulted in higher ED use.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.030
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.323
Teacher spread0.277 · 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 teacher head, 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

Citations32
Published2023
Admission routes3
Has abstractyes

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