Theorizing Organizational Change in EHR Implementation
Bibliographic record
Abstract
Electronic Health Record (EHR) implementation is recognized as a transformative process in healthcare organizations, reshaping workflows, decision-making, and professional roles. While prior research has examined adoption and success factors, less attention has been given to theorizing the organizational change mechanisms that unfold implementation. Existing studies often rely on deterministic aspects failing to capture the interdependencies between sociotechnical elements. In this paper, we conducted a scoping review of theoretical frameworks used to explain EHR change. Using the scoping review, we categorized these frameworks into organizational change models, configurational perspectives, institutional perspectives, decision-making theories, adoption models, and cognitive theories. Our findings reveal a key gap: existing theorization overlooks the relational dynamics among technology, actors, and organizational structures. We argue that a broader sociotechnical orientation is the appropriate approach to fully understand how EHRs bring about change over time, contributing to the digital transformation literature in healthcare.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".