Electronic Medical Record in Pediatric Intensive Care: Implementation Process Assessment
Bibliographic record
Abstract
The implementation of an electronic medical record (EMR) is a high-priority project in a majority of industrialized countries. The Healthcare Information and Management Systems Society (HIMSS) Analytics established an eight-stage EMR Adoption Model (EMRAM) to track progress against health care organizations across a country. In Canada, 36.5% of the hospitals are at the stage 3 or higher, whereas 0.2% have reached the seventh stage. To assess the impact on the safety and caregivers' satisfaction of a stage 7 EMR in a Quebec Pediatric Hospital initially at the EMRAM stage 3, a pilot customized implementation of paperless pediatric intensive care EMR was performed and evaluated. Six months after implementation, there was a nonsignificant decrease in severe medical incidents in comparison to the same period of time, the previous year. Most pediatric intensive care unit (PICU) staff were very or completely comfortable with the EMR, but the EMR satisfied 33.9% of all staff (everyday users [internal staff] and occasional user [external staff]) and 41.9% of internal staff only. The information gathered with this pilot EMR implementation using a 20-month preparation period and a continuous monitoring including change management ("living lab approach") after the "go live" helped in the success of the implementation but did not improve significantly caregivers' satisfaction, in the first 6 months of this dramatic change in practice.
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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 | 0.065 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".