Implementation of a nationwide electronic health record (EHR)
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the best practices applied during the implementation process of a national electronic health record (EHR) system. Furthermore, the main goal is to explore the knowledge gained by experts from leading countries in the field of nationwide EHR system implementation, focusing on some of the main success factors and difficulties, or failures, of the various implementation approaches. Design/methodology/approach To gather the necessary information, an international survey has been conducted with expert participants from 13 countries (Denmark, Austria, Sweden, Norway, the UK, Germany, the Netherlands, Switzerland, Canada, the USA, Israel, New Zealand and South Korea), who had been playing varying key roles during the implementation process. Taking into consideration that each system is unique, with each own (different) characteristics and many stakeholders, the methodological approach followed was not oriented to offer the basis for comparing the implementation process, but rather, to allow us better understand some of the pros and cons of each option. Findings Taking into account the heterogeneity of each country's financing mechanism and health system, the predominant EHR system implementation option is the middle-out approach. The main reasons which are responsible for adopting a specific implementation approach are usually political. Furthermore, it is revealed that the most significant success factor of a nationwide EHR system implementation process is the commitment and involvement of all stakeholders. On the other hand, the lack of support and the negative reaction to any change from the medical, nursing and administrative community is considered as the most critical failure factor. Originality/value A strong point of the current research is the inclusion of experts from several countries (13) spanning in four continents, identifying some common barriers, success factors and best practices stemming from the experience obtained from these countries, with a sense of unification. An issue that should never be overlooked or underestimated is the alignment between the functionality of the new EHR system and users' requirements.
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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 | 0.043 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".