The clinical and economic impact of a community-based, hybrid model of in-person and virtual care in a Canadian rural setting: A cross-sectional population-based comparative study
Notice bibliographique
Résumé
ABSTRACT Objectives To determine the clinical and economic impact of a community-based, hybrid model of in-person and virtual care by comparing health-system performance of the rural jurisdiction where this model was implemented with neighbouring jurisdictions without such a model and the broader regional health system. Design A cross-sectional comparative study. Setting Ontario, Canada, with a focus on three largely rural public health units from April 1, 2018, until March 31, 2021. Participants All residents of Ontario, Canada under the age of 105 eligible for the Ontario Health Insurance Plan (OHIP) during the study period. Interventions An innovative, community-based, hybrid model of in-person and virtual care, the Virtual Triage and Assessment Centre (VTAC), was implemented in Renfrew County, Ontario on March 27, 2020. Main outcome measures Primary outcome was change in emergency department (ED) visits anywhere in Ontario, secondary outcomes included changes in hospitalizations and health-system costs, using percent changes in mean monthly values of linked health-system administrative data for two years pre-implementation and one year post-implementation. Results Renfrew County saw larger declines in ED visits (−34.4%, 95% confidence interval -41.9% to -26.0%) and hospitalizations (−11.1%, 95% confidence interval -19.7% to -1.5%), and slower growth in health-system costs than other rural regions studied. VTAC patients’ low-acuity ED visits decreased by -32.9%, high-acuity visits increased by 8.2%, and hospitalizations increased by 30.0%. Conclusion After implementing VTAC, Renfrew County saw reduced ED visits and hospitalizations and slower health-system cost growth compared to neighbouring rural jurisdictions. VTAC patients experienced reduced unnecessary ED visits and increased appropriate care. Community-based, hybrid models of in-person and virtual care may reduce the burden on emergency and hospital services in rural, remote and underserved regions. Further study is required to evaluate potential for scale and spread. Trial registration Not applicable. STRENGTHS AND LIMITATIONS OF THE STUDY This study uses population-level health administrative data to investigate the empirical effects of a community-based, hybrid model of in-person and virtual care in rural, remote, and underserved communities, where access to comprehensive primary care is insufficient. Population-level data from administrative datasets were linked using unique encoded identifiers and analyzed at ICES, Ontario’s population health data steward. The intervention jurisdiction is compared with two similar adjoining jurisdictions and with the whole Province. Because of the relatively short time period studied — two years before the intervention and one year post — it remains to be seen whether the observed differences will persist over time. This study’s design does not allow firm inferences about causality; however, the observed changes are in the right temporal sequence and benefit from local comparisons of similar jurisdictions.
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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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 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 ».