A cancer care electronic medical record highly integrated into clinicians' workflow: users' attitudes pre-post implementation
Bibliographic record
Abstract
The purpose was to study users' attitudes towards an electronic medical record (EMR) closely integrated into the clinicians' cancer care workflow. The EMR, implemented in an ambulatory cancer care centre, was designed as a care pathway information system providing real-time support to the coordination of shared care processes involving all the care personnel. Mixed method pre-post study design was used. The study population consisted of all care personnel. A survey measured the quality attributes of the EMR, the clinical information it produces, the perceived usefulness of the system for supporting clinical data management tasks and the perceived impacts in terms of access and quality of care. The survey shows that users' attitudes towards the EMR (response rate of 71%) measured after the go-live were positive ranging from 3.42 to 3.95 on a 5-point scale. Besides, the content analysis of 33 pre-post interviews revealed five main themes: magnitude of the changes caused by the EMR; its innovative potential; its positive benefits; an ongoing growth in users' expectancies; and the burden associated with the time required to operate the EMR. In sum, the study shows that users can largely apply innovative uses of information technologies that automate their clinical processes.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".