Replacing an Inpatient Electronic Medical Record
Bibliographic record
Abstract
OBJECTIVE: Since it is important to develop strategies for the successful implementation of electronic clinical information systems, the aim of this study is to explore where, and to what extent, users' attitudes toward the former system that is being replaced may vary. METHODS: A cross-sectional survey of 346 nurses and physicians practicing in two Canadian teaching hospitals resulted in a total response rate of 63%. User attitudes were measured in three dimensions: a) user satisfaction with the system's quality attributes, b) perceived system usefulness, and c) perceived impact on quality of care and patient safety. The current system (the one being replaced) was analyzed as a dual system composed of both paper-based and electronic records. RESULTS: The results on user satisfaction demonstrate a wide variation in opinions, with satisfaction ranging from 4.2 to 7.7 on a 10-point disagree-agree, Likert scale. The quality attributes varied by record type, with differences that were systematically in favor of the electronic record component, which received higher scores. The results also highlighted large differences by user group. Physicians and nurses systematically rated the two record formats differently. The nurses were more satisfied with the attributes of the paper-based record. Multivariate regression analyses results also revealed strong interdependencies among the three dimensions of user attitudes, to the extent that perceived system usefulness was strongly correlated with system quality attributes and the system outcomes were also correlated, although less strongly, with the two former system dimensions. CONCLUSION: Understanding users' attitudes toward a clinical information system in use, both in its paper and electronic aspects, is crucial for developing more successful implementation strategies for electronic record systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".