Evaluating EMR interoperability across Canadian jurisdictions: Maturity model and roadmap toward integration
Bibliographic record
Abstract
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.
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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 | 0.062 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".