Experiences With Internet Triaging of 9498 Outpatients Daily at the Largest Public Hospital in Taiwan During the COVID-19 Pandemic: Observational Study
Bibliographic record
Abstract
BACKGROUND: During pandemics, acquiring outpatients' travel, occupation, contact, and cluster histories is one of the most important measures in assessing the disease risk among incoming patients. Previous means of acquiring this information in the examination room have been insufficient in preventing disease spread. OBJECTIVE: This study aimed to demonstrate the deployment of an automatic system to triage outpatients over the internet. METHODS: An automatic system was incorporated in the existing web-based appointment system of the hospital and deployed along with its on-site counterpart. Automatic queries to the virtual private network travel and contact history database with each patient's national ID number were made for each attempt to acquire the patient's travel and contact histories. Patients with relevant histories were denied registration or entry. Text messages were sent to patients without a relevant history for an expedited route of entry if applicable. RESULTS: A total of 127,857 visits were recorded. Among all visits, 91,195 were registered on the internet. In total, 71,816 of them generated text messages for an expedited route of entry. Furthermore, 65 patients had relevant histories, as revealed by the virtual private network database, and were denied registration or entry. CONCLUSIONS: An automatic triage system to acquire outpatients' relevant travel and contact histories was deployed rapidly in one of the largest academic medical centers in Taiwan. The updated system successfully denied patients with relevant travel or contact histories entry to the hospital, thus preventing long lines outside the hospital. Further efforts could be made to integrate the system with the electronic medical record system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".