Tensions in large-scale electronic health record implementations: insights from a meta-synthesis
Bibliographic record
Abstract
OBJECTIVE: To synthesize knowledge on tensions characterizing large-scale electronic health record (EHR) implementations. MATERIALS AND METHODS: A qualitative meta-synthesis was conducted by searching Scopus, Web of Science, MEDLINE, and CINAHL databases to find studies focusing on large-scale EHR implementations in OECD countries. An extraction table was completed to describe key characteristics of cases, and instances of tensions were extracted within each study based on a conceptual definition. RESULTS: Twenty-six qualitative studies were included, covering eleven unique large-scale EHR implementation projects. Cases were in Europe (n = 6), North America (n = 4), and Southeast Asia (n = 1). Analysis yielded twenty-one types of tensions associated with five primary objects: people, power, resources, system, and vision. Twelve tensions were found in multiple cases while fifteen were associated with more than one object. DISCUSSION: Results are aligned with the notion that tensions are inherent to organizational phenomena, showcasing their enduring nature across geographic, temporal, and technological contexts. The diversity of these tensions and their associated object(s) refer to critical, interrelated components of EHR systems implementation that are exacerbated in large-scale projects, and which can affect the implementation across its entire lifecycle. CONCLUSION: Stakeholders involved in projects to modernize healthcare through the large-scale implementation of EHRs are prone to experience multiple tensions. Attention to the emergence of the tensions identified in this study helps to understand their impacts on projects and stakeholders. Tensions and their associated objects undergird the sociotechnical nature of these complex projects and the need to manage them effectively.
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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 | 0.182 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.038 | 0.030 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".