Handheld computers in critical care
Bibliographic record
Abstract
BACKGROUND: Computing technology has the potential to improve health care management but is often underutilized. Handheld computers are versatile and relatively inexpensive, bringing the benefits of computers to the bedside. We evaluated the role of this technology for managing patient data and accessing medical reference information, in an academic intensive-care unit (ICU). METHODS: Palm III series handheld devices were given to the ICU team, each installed with medical reference information, schedules, and contact numbers. Users underwent a 1-hour training session introducing the hardware and software. Various patient data management applications were assessed during the study period. Qualitative assessment of the benefits, drawbacks, and suggestions was performed by an independent company, using focus groups. An objective comparison between a paper and electronic handheld textbook was achieved using clinical scenario tests. RESULTS: During the 6-month study period, the 20 physicians and 6 paramedical staff who used the handheld devices found them convenient and functional but suggested more comprehensive training and improved search facilities. Comparison of the handheld computer with the conventional paper text revealed equivalence. Access to computerized patient information improved communication, particularly with regard to long-stay patients, but changes to the software and the process were suggested. CONCLUSIONS: The introduction of this technology was well received despite differences in users' familiarity with the devices. Handheld computers have potential in the ICU, but systems need to be developed specifically for the critical-care environment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".