Evaluating EMR interoperability across Canadian jurisdictions: Maturity model and roadmap toward integration
Notice bibliographique
Résumé
Purpose: Canada has made significant progress in adopting electronic medical records (EMRs), with usage rising from 40% in the early 2000s to over 90% by 2021, according to the Commonwealth Fund. Despite this, many EMRs still function as “electronic paper charts” with limited interoperability—the ability to exchange data across healthcare settings. This lack of interoperability impacts patient care, operational efficiency, and decision-making. While Canada Health Infoway and various provinces have invested in EMR integration, progress is hindered by decentralized control each jurisdiction has over its health IT strategy. Understanding EMR interoperability maturity and regional barriers is essential for driving change. No comprehensive, pan-Canadian landscape of EMR interoperability has been documented. In collaboration with the Canadian Institute for Health Information (CIHI) and Canada Health Infoway, our study aimed to (i) assess EMR interoperability maturity across the country, (ii) explore the unique enablers and barriers in each region, and (iii) inform jurisdictional roadmaps for digital health interoperability. Approach: To evaluate EMR interoperability, we leveraged a structured maturity model with expert interviews across the country. First, we adapted international frameworks (e.g., USAID MEASURE, HIMSS EMRAM) to create a Canadian-specific maturity model, focused on two key areas: interoperability enablers—covering governance, standards, incentives, and infrastructure—and interoperability status, which examines integration between community EMRs, hospital EMRs, patient portals, and health system planning. We then conducted ~20 interviews with key stakeholders across all jurisdictions, including healthcare leaders, policymakers, and technology vendors. These interviews provided rich, region-specific insights into the existing infrastructure and key challenges faced by each jurisdiction in advancing EMR interoperability. Findings: The findings highlight the uneven landscape of digital health infrastructure across Canada. Alberta and Newfoundland and Labrador were identified as leaders, with strong governance structures and integrated systems that support seamless data sharing. On the other hand, provinces like Ontario and Quebec face significant fragmentation in their EMR systems, limiting their ability to scale interoperability. The key enablers of successful interoperability included centralized governance, well-defined standards, and vendor cooperation through initiatives like Health Information Exchanges (HIEs). However, barriers such as fragmented EMR markets, inconsistent data standards, and lack of incentives continue to pose significant challenges, particularly in provinces with more decentralized systems. For vendors and policymakers, these findings underscore the need for customized solutions. Provinces that have already built robust technical infrastructure, such as Alberta, can focus on scaling existing solutions, while others, like Ontario, may need to prioritize harmonizing fragmented systems and establishing clear governance. Tailoring roadmaps to the specific needs of each province, and ensuring adequate resources and vendor alignment, will be key to achieving nationwide interoperability. Conclusion: The landscape of EMR interoperability across Canada is highly varied. Some provinces have made great strides, while others are just beginning the journey toward integration. Policymakers and vendors have a crucial role to play in addressing these challenges by building flexible, localized strategies that meet the unique needs of each jurisdiction. Sharing best practices and investing in standardized solutions, will be essential in creating a sustainable, interoperable digital health ecosystem.
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 | 0,062 | 0,098 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,014 | 0,016 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,011 | 0,008 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».