Information Technology Systems for Critical Care Triage and Medical Response During an Influenza Pandemic: A Review of Current Systems
Bibliographic record
Abstract
OBJECTIVES: To assess local, state, federal, and global pandemic influenza preparedness by identifying pandemic plans at the local, state, federal, and global levels, and to identify any information technology (IT) systems in these plans to support critical care triage during an influenza pandemic in the Canadian province of Ontario. METHODS: The authors used advanced MEDLINE and Google search strategies and conducted a comprehensive review of key pandemic influenza Web sites. Descriptive data extraction and analysis for IT systems were conducted on all of the included pandemic plans. RESULTS: A total of 155 pandemic influenza plans were reviewed: 29 local, 62 state, 63 federal, and 1 global. We found 70 plans that examined IT systems (10 local, 33 state, 26 federal, 1 global), and 85 that did not (19 local, 29 state, 37 federal). Of the 70 plans, 64 described surveillance systems (10 local, 32 state, 21 federal, 1 global), 2 described patient data collection systems (1 state, 1 federal); 4 described other types of IT systems (4 federal), and none were intended for triage. CONCLUSIONS: Although several pandemic plans have been drafted, the majority are high-level general documents that do not describe IT systems. The plans that discuss IT systems focus strongly on surveillance, which fails to recognize the needs of a health care system responding to an influenza pandemic. The best examples of the types of IT systems to guide decision making during a pandemic were found in the Kansas and the Czech Republic pandemic plans, because these systems were designed to collect both patient and surveillance data. Although Ontario has yet to develop such an IT system, several IT systems are in place that could be leveraged to support critical care triage and medical response during an influenza pandemic.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".