Examining the relationship between the “real world” adoption of digital health tools and primary care experience
Notice bibliographique
Résumé
Background: Patient experience is a crucial measure of patient-centeredness and quality care delivery. Digital health may contribute to patient experience by offering tailored and accessible avenues of care. Purpose: I explored how access to digital health, including telehealth, electronic health records, and online booking, may be associated with improved primary care experience for Ontario adults. Methods: This cross-sectional study included Ontario adults (16 years or older) who responded to waves 27 to 29 of the Health Care Experience Survey (HCES) between May 2019 and February 2020. Adults who did not see their primary care provider within the past 12 months or did not have a primary care provider were excluded. Outcomes included a summed patient experience score derived from five HCES experience-related questions and time to appointment for a health concern. Associations between outcomes and digital health interventions were tested through chi-square tests and logistic regression while adjusting for confounders and stratifying by health care utilization. Results: 3,700 participants met the inclusion criteria, where 2204 remotely communicated with their primary care provider (59.6%), 98 digitally accessed health records (2.6%), and 120 booked an appointment online (3.2%). We observed no significant associations between digital health tools and patient experience or time to appointments through chi-square tests. Participants with over three primary care visits in the past year who accessed online booking were 84% less likely to report poorer experience scores than participants without online booking access [Adjusted OR 0.16, 95% CI 0.02 – 0.56, p < 0.05]. Participants with three or fewer primary care encounters who accessed online booking, compared to the same reference group, were 72% less likely to report having a same or next day appointment with their primary care provider [Adjusted OR 0.25, 95% CI 0.08 – 0.64, p < 0.01]. Significant associations were observed between other sociodemographic factors and patient experience and access to care outcomes. Interpretation: The associations between digital health access and patient experience and access to care were inconsistent across different analyses. Despite experimental studies observing the benefits of digital health adoption in primary care, the effect is unclear in the real-world context. Furthermore, drawing conclusions on the relationship between digital health and quality care outcomes was limited due to the lack of adoption of digital health before the COVID-19 pandemic. As digital health adoption grows, future research should utilize the availability of further data to evaluate the effectiveness of digital health in Ontario primary care.
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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 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».