Real-time automated paging and decision support for critical laboratory abnormalities
Bibliographic record
Abstract
BACKGROUND: For patients with critical laboratory abnormalities, timely clinical alerts with decision support could improve management and reduce adverse events. METHODS: The authors developed a real-time clinical alerting system for critical laboratory abnormalities. The system sent alerts to physicians as text messages to a smartphone or alphanumeric pager. Decision support was available via smartphone or hospital intranet. The authors evaluated the system in a prospective controlled stepped-wedge study with blinded outcome assessment in general internal medicine units at two academic hospitals. The outcomes were the proportion of potential clinical actions that were actually completed in response to the alert, and adverse events (worsening of condition or complications related to treatment of the condition). RESULTS: The authors evaluated 498 laboratory conditions on 271 patients. Overall, only 50% of potential clinical actions were carried out, and there were adverse clinical events within 48 h for 36% of the laboratory conditions. The median (IQR) proportion of potential clinical actions that were actually completed was 50% (33-75%) with alerting system on and 50% (33-100%) with alerting system off (p=0.94, Wilcoxon rank sum test). When the alerting system was on (n=164 alerts) there were 67 adverse events within 48 h of the alerts (42%). When the alerting system was off (n=334 alerts), there were 112 adverse events within 48 h (33%; difference: 9% higher with alerting system on, p=0.06). CONCLUSIONS: The provision of real-time clinical alerts and decision support for critical laboratory abnormalities did not improve clinical management or decrease adverse events.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".