Changes in outpatient care patterns and subsequent outcomes during the COVID-19 pandemic: A retrospective cohort analysis from a single payer healthcare system
Notice bibliographique
Résumé
ABSTRACT Background There have been rapid shifts in outpatient care models during the COVID-19 pandemic but the impact of these changes on patient outcomes are uncertain. We designed this study to examine ambulatory outpatient visit patterns and outcomes between March 1, 2019 to February 29, 2020 (pre-pandemic) and from March 1, 2020 to February 28, 2021 (pandemic). Methods We conducted a population-based retrospective cohort study of all 3.8 million adults in the Canadian province of Alberta, which has a single payer healthcare system, using linked administrative data. We examined all outpatient physician encounters (virtual or in-person) and outcomes (emergency department visits, hospitalizations, or deaths) in the next 30- and 90-days. Results Although in-person outpatient visits declined by 38.9% in the year after March 1, 2020 (10,142,184 vs. 16,592,599), the increase in virtual visits (7,152,147; 41.4% of total) meant that total outpatient encounters increased by 4.1% in the first year of the pandemic. Outpatient care and prescribing patterns remained stable for adults with ambulatory-care sensitive conditions (ACSC): 97.2% saw a primary care physician (median 6 visits), 59.0% had at least one specialist visit, and 98.5% were prescribed medications (median 9) in the year prior to the pandemic compared to 96.6% (median 3 in-person and 2 virtual visits), 62.6%, and 98.6% (median 8 medications) during the first year of the pandemic. In the first year of the pandemic, virtual outpatient visits were associated with less subsequent healthcare encounters than in-person ambulatory visits, particularly for patients with ACSC (9.2% vs. 10.4%, aOR 0.89 [95% confidence interval 0.87-0.92] at 30 days and 26.9% vs. 29.3%, aOR 0.93 [0.92-0.95] at 90 days). Conclusions The shifts in outpatient care patterns caused by the COVID-19 pandemic did not disrupt prescribing or follow-up for patients with ACSC and did not worsen post-visit outcomes. Funding None Registration None KEY MESSAGES What is already known on this topic There have been rapid shifts in outpatient care models during the COVID-19 pandemic but outcomes are uncertain. What this study adds Total outpatient encounters increased by 4% in the first year of the pandemic due to a rapid increase in virtual visits (which made up 41% of all outpatient encounters). Prescribing patterns and frequency of follow-up were similar in the first year after onset of the pandemic in adults with ambulatory-care sensitive conditions. Compared to in-person visits, virtual outpatient visits were associated with less subsequent healthcare encounters, particularly for patients with ambulatory-care sensitive conditions (11% less at 30 days and 7% less at 90 days). How this study might affect research, practice or policy Our data provides reassurance that the shifts in outpatient care patterns caused by the COVID-19 pandemic did not negatively impact follow-up, prescribing, or outcomes for patients with ACSC. Further research is needed to define which patients and which conditions are most suitable for virtual outpatient visits and, as with all outpatient care, the optimal frequency of such visits.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».