Supporting Clinical Practice at the Bedside Using Wireless Technology
Bibliographic record
Abstract
OBJECTIVES: Despite studies that show improvements in both standards of care and outcomes with the judicious application of clinical practice guidelines (CPGs), their clinical utilization remains low. This randomized controlled trial examined the use of a wirelessly networked mobile computer (MC) by physicians at the bedside with access to an emergency department information system, decision support tools (DSTs), and other software options. METHODS: Each of ten volunteer emergency physicians was randomized using a matched-pair design to work five shifts in standard fashion (desktop computer [DC] access) and five shifts with a wirelessly networked MC. Work pattern issues and electronic CPG/DST use were compared using end-of-shift satisfaction questionnaires and review of a CPG/DST database. Repeated-measures analysis of variance was used to examine between-shift differences. RESULTS: A total of 100 eight-hour shifts were evaluated; 99% compliance with postshift questionnaires was achieved. Using a seven-point Likert scale (MC values first), MCs were rated as being as fast (5.04 vs. 4.54; p=0.13) and convenient (5.08 vs. 4.14; p=0.07) as DCs. Overall, physicians rated MCs to be less efficient (3.18 vs. 4.30; p=0.02) but encouraged more frequent use of DSTs (4.10 vs. 3.47; p=0.03) without impacting doctor-patient communication (2.78 vs. 2.96; p=0.51). During the study period, physician use of an intranet Web application (eCPG) was more frequent during shifts assigned to the MC when compared with the DC (eCPG uses/shift, 3.6 vs. 2.0; p=0.033). CONCLUSIONS: The MC technology permitted physicians to access information at the bedside and increased the use of CPG/DST tools. According to physicians, patients appeared to accept their use of information technology to assist in decision making. Development of improved computer technology may address the major limitation of MC portability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".