Characterization of Public Health Alerts and Their Suitability for Alerting in Electronic Health Record Systems
Bibliographic record
Abstract
Public health agencies including federal, state, and local governments routinely send out public health advisories and alerts via e-mail and text messages to health care providers to increase awareness of public health events and situations. Agencies must ensure that practitioners have timely and accessible information at the critical point-of-care. Electronic health record (EHR) systems have the potential to alert physicians of emerging health conditions deemed important for public health at the most critical time of need. To understand how public health agencies can leverage existing alerting mechanisms in EHR systems, it is important to understand characteristics of public health alerts to determine their suitability for alerting in EHR systems. Authors conducted a review and analysis of public health alerts for a 3-year period to identify critical data attributes necessary to support public health alerting in EHR systems. The alerts were restricted to those most relevant for clinical care. The results showed that there is an opportunity for disseminating actionable information to clinical practitioners at the point of care to guide care and reporting. Public health alerts in EHR systems can be useful in reporting, recommending specific tests, as well as suggesting secondary prevention.
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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 | 0.024 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".