Impact of a Clinical Information System on Multitasking in Two Intensive Care Units
Bibliographic record
Abstract
Health Care Providers (HCPs) in Intensive Care Units (ICUs) communicate effectively to coordinate timely patient care. HCPs rapidly switch between patient care, documentation and communication tasks such that they are completed simultaneously or nearly simultaneously, a phenomenon termed multitasking. An electronic charting tool or Critical Care clinical Information System (CCIS) may facilitate information sharing, but system related changes in multitasking have not been investigated. Trained observers followed physicians, nurses, respiratory therapists, and unit clerks in two ICUs and recorded their tasks. Observations were completed before the introduction of the CCIS at 3 and at 12 months afterward, using the Work Observation Method By Activity Timing (WOMBAT). Amounts of time HCPs spent performing multitasking before and after the CCIS introduction were compared, along with the tasks composing multitasking events. Before the CCIS introduction, respiratory therapists, nurses, and physicians spent approximately 30-40% of their time multitasking, whereas unit clerks spent less time multitasking (14%-18%). Percentages of time spent multitasking decreased to values between 10% and 25%. Documentation and communication tasks accounted for large proportions of the multitasking reduction. Cognitive burdens associated with learning new documentation methods, or constraints of charting at bedside terminals may be causes of observed reductions in multitasking. Perceptions of poorer communication, lower productivity, and less staff acceptance of the CCIS may result.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".