Navigating large-scale EHR implementations in public health systems: Lessons learned and recommendations from a rapid review
Bibliographic record
Abstract
Objective: This review systematically synthesizes empirical evidence from past NEHR initiatives to identify critical gaps between knowledge and practice and provide actionable insights for policymakers, health IT leaders, and practitioners. Materials and Methods: A rapid review approach was employed, focusing on qualitative content analysis of empirical studies published between 2010 and 2024. The search covered the Scopus, PubMed, Medline, and CINAHL databases. A total of 24 studies met the eligibility criteria and were analyzed across key dimensions. Results: Our analysis reveals that successful NEHR implementation hinges on three interdependent factors: (1) Stakeholder engagement and governance—meaningful clinician involvement and adaptive leadership strategies are crucial for system adoption; (2) Institutional and cultural alignment—the tension between centralized mandates and local adaptation must be carefully managed; and (3) Technological and process standardization—balancing interoperability with customizability remains a persistent challenge. Notably, rigid top-down implementations often face resistance, whereas hybrid “middle-out” approaches tend to facilitate smoother transitions. Conclusions: NEHR deployments require a nuanced approach that integrates strategic decision-making, continuous stakeholder engagement, and flexible governance models. Policymakers and project leaders should prioritize participatory implementation strategies, adaptive standardization, and mechanisms for iterative learning to enhance the sustainability and effectiveness of these systems.
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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 | 0.072 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".