Reorganizing Care With the Implementation of Electronic Medical Records: A Time-Motion Study in the PICU*
Bibliographic record
Abstract
OBJECTIVES: To assess caregivers' patient care time before and after the implementation of a reorganization of care plan with electronic medical records. DESIGN: A prospective, observational, time-motion study. SETTING: A level 3 PICU. PARTICIPANTS: Nurses and orderlies caring for intubated patients during an 8-hour work shift before (2008-2009) and after (2016) implementation of reorganization of care in 2013. INTERVENTIONS: The reorganization plan included improved telecommunication for healthcare workers, increased tasks delegated to orderlies, and an ICU-specific electronic medical record (Intellispace Critical Care and Anesthesia information system, Philips Healthcare). MEASUREMENTS AND MAIN RESULTS: Time spent completing various work tasks was recorded by direct observation, and proportion of time in tasks was compared for each study period. A total of 153.7 hours was observed from 22 nurses and 14 orderlies. There was no significant difference in the proportion of nursing patient care time before (68.8% [interquartile range, 48-72%]) and after (55% [interquartile range, 51-57%]) (p = 0.11) the reorganization with electronic medical record. Direct patient care task time for nurses was increased from 27.0% (interquartile range, 30-37%) before to 34.7% (interquartile range, 33-75%) (p = 0.336) after, and indirect patient care tasks decreased from 33.6% (interquartile range, 23-41%) to 18.6% (interquartile range, 16-22%) (p = 0.036). Documentation time significantly increased from 14.5% (interquartile range, 12-22%) to 26.2% (interquartile range, 23-28%) (p = 0.032). Nursing productivity ratio improved from 28.3 to 26.0. A survey revealed that nursing staff was satisfied with the electronic medical record, although there was a concern for the maintenance of oral communication in the unit. CONCLUSIONS: The reorganization of care with the implementation of an ICU-specific electronic medical record in the PICU did not change total patient care provided but improved nursing productivity, resulting in improved efficiency. Documentation time was significantly increased, and concern over reduced oral communication arose, which should be a focus for future electronic improvement strategies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".