An Organizational Culture-Based Theory of Clinical Information Systems Implementation in Hospitals
Bibliographic record
Abstract
We propose an organizational culture-based explanation of the level of difficulty of clinical information system (CIS) implementation and of the practices that can contribute to reduce the level of difficulty of this process. Adopting an analytic induction approach, we developed initial theoretical propositions based on a three-perspective conceptualization of organizational culture: integration, differentiation, and fragmentation. Using data from three cases of CIS implementation, we first performed a deductive analysis to test our propositions on the relationships between culture, CIS characteristics, implementation practices, and the level of implementation difficulty. Then, applying an inductive analysis strategy, we re-analyzed the data and developed new propositions. Our analysis shows that four values play a central role in CIS implementation. Two values, quality of care and efficiency of clinical practices, are key from an integration perspective; two others, professional status/autonomy and medical dominance, are paramount from a differentiation perspective. A fragmentation perspective analysis reveals that hospital users sometimes have ambiguous interpretations of some CIS characteristics and/or implementation practices in terms of their consistency with these four values. Overall, the proposed theory provides a rich explanation of the relationships between CIS characteristics, implementation practices, user values, and the level of difficulty of the implementation process.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| 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".