Real-time clinical alerting: effect of an automated paging system on response time to critical laboratory values--a randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Timely and reliable communication of critical laboratory values is a Joint Commission National Patient Safety Goal. The objective was to evaluate the effect of an automated system for paging critical values directly to the responsible physician. METHODS: A randomised controlled trial on the general medicine clinical teaching units at an urban academic hospital was conducted from February to May 2006; the unit of randomisation was the critical laboratory value. The intervention was an automated paging system that sent the critical value directly to the responsible physician's pager. The control arm was usual care, which was a telephone call to the patient's ward by the laboratory technician. The primary outcome was response time, defined as the interval between acceptance of the critical value into the laboratory information system to the writing of an order on the patient's chart in response to the critical value. If the time of order was not documented, the time of administration of treatment was used to calculate response time. RESULTS: For primary analysis, 165 critical values were evaluated on 108 patients with full response time data. The median response time was 16 min (IQR 2-141) for the automated paging group and 39.5 min (IQR 7-104.5) for the usual care group (p=0.33). CONCLUSIONS: The automated paging system reduced the length of time physicians took to respond to critical laboratory values, but this difference was not statistically significant. Future reseach should evaluate the effects of alerts for conditions that currently do not generate a phone call and the addition of real-time decision support to the critical value alerts.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".