Pioneering Digital Health in NB: A Pathway to Transformation in Health Care Delivery
Bibliographic record
Abstract
Digital health has emerged as a crucial component of health care delivery in New Brunswick (NB), especially in response to the COVID-19 pandemic. This analysis examines the digital health reform in NB, focusing on the implementation and outcomes of the Virtual Care program and MyHealthNB application. The reform aimed to enhance access to health care services, particularly for rural and remote populations, by leveraging digital technologies. Key objectives included improving patient-centred care, supporting seniors, and integrating digital health solutions into the provincial health care system. The analysis highlights the factors influencing the reform, including demographic trends, technological advancements, and stakeholder engagement. It also discusses the challenges encountered, such as provider resistance and interoperability issues, and evaluates the program’s impact on health care delivery and patient outcomes. The adoption of tools like electronic health records (EHRs), virtual care platforms, and the MyHealthNB portal marked a shift toward integrated, sustainable service delivery. La santé numérique est devenue un élément crucial de la prestation de soins de santé au Nouveau-Brunswick (NB), en particulier en réponse à la pandémie de COVID-19. Cette analyse porte sur la réforme de la santé numérique au Nouveau-Brunswick, en se concentrant sur la mise en œuvre et les résultats du programme de soins virtuels et de l’application MyHealthNB. La réforme visait à améliorer l'accès aux services de santé, en particulier pour les populations rurales et isolées, en tirant parti des technologies numériques. Les principaux objectifs étaient d'améliorer les soins centrés sur le patient, de soutenir les personnes âgées et d’intégrer les solutions de santé numérique dans le système de santé provincial. L’analyse met en évidence les facteurs qui ont influencé la réforme, notamment les tendances démographiques, les avancées technologiques et l’engagement des parties prenantes. Elle aborde également les difficultés rencontrées, telles que la résistance des prestataires et les problèmes d’interopérabilité, et évalue l’impact du programme sur la prestation des soins de santé et les résultats pour les patients. L’adoption d’outils tels que les dossiers médicaux électroniques (DME), les plateformes de soins virtuels et le portail MyHealthNB a marqué un tournant vers une prestation de services intégrée et durable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".