Leveraging Innovation to Improve Rural and Remote Emergency Health Services in British Columbia
Notice bibliographique
Résumé
Background: Almost twenty percent of BC population live in rural settings; yet accessing healthcare, particularly emergency health services (EHSs), is challenging for rural and remote populations as they continue to experience systemic inequities, higher incidence of chronic illnesses, and lower access to healthcare services. Access to EHSs is essential for integrated health services for these populations. Despite the challenges, healthcare providers have demonstrated resiliency in delivering EHSs in rural and remote communities through innovative approaches such as Real-time Virtual Support (RTVS). Additionally, the BC government has made significant efforts to improve EHSs by increasing the budget for innovation (e.g., virtual care and broadband technology) and emergency transport services, however, there is limited information on these innovations in the literature. This study aims to explore and understand how current innovations emerged and evolved in rural and remote EHSs to identify what works, for whom, and in which contexts, and to develop recommendations for policymakers and decision-makers to leverage innovation in rural and remote EHSs. Approach: The case study methodology, with a focus on narrative inquiry, was employed to study three rural and remote cases: two in northern BC and one in interior BC. Mixed methods were used to collect data over two phases. Phase I involved descriptive data collection and community visits. Phase II involved: semi-structured interviews with policymakers, decision-makers, managers, health providers, and administrative staff (n=3); focus groups with patients and community members (n=4); and aggregate administrative data collection from various relevant organizations (e.g., health authorities). Thematic analysis was conducted to identify common themes in the qualitative data. Quantitative data analysis, using descriptive statistics, is in process. A preliminary report was developed and shared with each case study participants through an in-person follow-up dialogue (n=20) to discuss the results and co-create actions moving EHSs innovations forward. Final individual case study reports and cross-case reports will be shared with the communities. Results: Qualitative data showed that various innovations such as Real Time Virtual Support ( pathways, virtual care, emergency physician online support, mechanical CPR devices, translation apps, and electronic triage and transfer systems were being used to provide EHSs. These innovations, particularly RTVS, equipped health providers with additional support, increased community membersaccess to EHSs, and reduced unnecessary transfer of patients out of the community. However, barriers to innovations such as limited resources (e.g., funding, digital health inequities, innovations for mental health services and support), cross-jurisdictional policies, and staff shortages were highlighted. Recommendations to enhance innovations in EHSs such as increasing funding for rural infrastructures, expanding RTVS services, exploring the alignment of policies, planning proactively, and collaborating with the local government were identified. Quantitative data analysis is currently in progress and will be included in the presentation. Implications: Recent innovations in EHSs have improved access to integrated care in rural and remote communities. Results of this study will guide policymakers and decision-makers to advance, adapt, and scale innovation and facilitate equitable access to EHSs for rural, remote, and Indigenous communities.
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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 | 0,005 |
| 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 | 0,007 | 0,003 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».