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Enregistrement W4307523459 · doi:10.1101/2022.10.20.22281298

Virtual care use prior to emergency department admissions during a stable COVID-19 period in Ontario, Canada

2022· preprint· en· W4307523459 sur OpenAlexaffabout
Vess Stamenova, Cherry Chu, Emily Borgundvaag, Cathleen Fleury, Janette Brual, Onil Bhattacharyya, Mina Tadrous

Notice bibliographique

RevuemedRxiv · 2022
Typepreprint
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensUniversity of TorontoWomen's College Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicineEmergency departmentTelemedicineTriageEmergency medicinePandemicCoronavirus disease 2019 (COVID-19)Ambulatory careHealth careOutpatient visitsMedical emergencyPopulationHospital admissionOutpatient clinicRetrospective cohort studyInternal medicineNursingDisease

Résumé

récupéré en direct d'OpenAlex

Abstract Background The increased use of telemedicine during the pandemic has led to concerns about potential increased emergency department (ED) admissions and outpatient service use prior to such admissions. We examined the frequency of telemedicine use prior to ED admissions and characterized the patients with prior telemedicine use and the physicians who provided these outpatient visits. Methods We conducted a retrospective, population-based, cross-sectional analysis using linked health administrative data in Ontario, Canada to identify patients who had an ED admission between July 1 and September 30, 2021 and patients with an ED admissions during the same period in 2019. We grouped patients based on their use of outpatient services in the 7 days prior to admission and reported their sociodemographic characteristics and healthcare utilization. Results There were 1,080,334 ED admissions in 2021 vs. 1,113,230 in 2019. In 2021, 74% of these admissions had no prior outpatient visits (virtual or in-person) within 7 days of admission, compared to 75% in 2019. Only 3% of ED admissions had both virtual and in-person visits in the 7 days prior to ED admission. Patients with prior virtual care use were more likely to be hospitalized than those without any outpatient care (13% vs 7.7.%). Interpretation The net amount of ED admissions and outpatient care prior to admission remained the same over a period of the COVID-19 pandemic when cases were relatively stable. Virtual care seems to be able to appropriately triage patients to the ED and may even prove beneficial for diverting patients away from the ED when an ED admission is not appropriate. The COVID-19 pandemic has led to the emergence of standard use of telemedicine in health care across the globe(1,2). In Ontario, Canada the proportion of ambulatory visits completed virtually has been maintained at slightly above 50% from 2020 to 2021 (3). Despite its widespread adoption, it is still unclear when virtual visits are clinically appropriate and how such wide use of telemedicine impacts patient outcomes and healthcare utilization metrics. Before the pandemic, there had been concerns that telemedicine may lead to an increased use of outpatient services with patients having both a virtual and an in-person visit for the same clinical issue(4,5). For example, pre-pandemic data (2007-2016) from Manitoba showed that telemedicine users had on average 1.3 times more ambulatory visits than non-users.(6) In addition, studies have produced mixed evidence with regard to the effect of telemedicine on urgent services such as emergency department (ED) admissions and hospitalizations (7). Many of the studies reported in the literature are based on data from site-specific programs and therefore have limited generalizability. Finally, policymakers and some physicians have become concerned that the high rates of telemedicine during COVID-19 have led to an increase in emergency department admissions because of poor access to in-person outpatient care (8). This concern is exacerbated when one considers rural and lower socioeconomic status patients who already had poor access to care before the pandemic(9). Combined with reports of lower uptake of telemedicine among these patients(10,11), it is not clear how the transition of care from in-person to virtual impacts ED use. The high adoption of telemedicine during the pandemic, in the context of a publicly funded healthcare system allowing us access to most visits across the entire population, offers a unique opportunity to examine the frequency of telemedicine use prior to ED admissions. Therefore, the goal of this study was to characterize the frequency and modality (in-person vs virtual) of outpatient care prior to ED admissions. We examined whether there was an overall increase in outpatient visits prior to ED admissions during a period of the pandemic when access to telemedicine was available compared to a seasonality matched period before the pandemic where access to telemedicine was quite limited. We also aimed to characterize the patients who had a telemedicine visit prior to an ED admission vs. those who had an in-person visit and the physicians who saw patients with virtual only visits prior to their ED admission compared to those who saw patients virtually or in-person prior to their ED admission.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil0,175

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0020,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,069
Tête enseignante GPT0,360
Écart entre enseignants0,291 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission2
Résumé présentoui

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