Determinants of Clinical Information System Post-Adoption Success
Bibliographic record
Abstract
The diffusion of information technology (IT) in healthcare systems to support clinical processes makes the evaluation of physician and nurse post-adoption an important challenge for clinical information systems (CIS). This paper examines the relationships between the determinants of success of a CIS based on an expectation-confirmation paradigm in a cross-sectional survey performed at the Sherbrooke University Hospital (CHUS). 32.2% (161) of physicians and 27.1% (352) of nurses responded to the survey questionnaires. Results suggested that physician and nurse satisfaction is determined differently according to post-adoption expectations: compatibility, confirmation of expectations, usefulness, ease of use, and support. The best predictor of physician satisfaction was perceived usefulness (r=.25, p=.0003) whereas for nurses it was ease of use (r=.18, p=.0003). Confirmation of expectations was strongly associated with each post-adoption expectation and positions its importance in CIS design and redesign. This study draws attention to the differences between physician and nurse perceptions of information technology and emphasizes post-adoption evaluation to measure CIS success. Physicians and nurses post-adoption expectations were key factors to warn again potential discontinuance.
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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 | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".