534Reduction in Patient Isolation Days for Suspected Influenza: Impact of Automated Influenza Testing
Bibliographic record
Abstract
Background. During influenza season hospital admissions increase, as does the need for patient isolation. Increased patient volumes and the burden of isolation may adversely affect patient care. Newer testing methodologies that reduce TAT should reduce isolation needs, but formal studies examining this hypothesis are lacking. This study was conducted to determine whether adoption of an automated influenza diagnostic test (Xpert Flu assay, Cepheid) would reduce turn-around time (TAT) and patient isolation days. Methods. This study was conducted at a 450 bed acute care hospital in Toronto. Overall isolation days, indications for isolation, and the TAT for influenza results were compared between the 2012/13 and 2013/14 influenza seasons. Between seasons, our testing methodology changed from a conventional reverse-transcription PCR assay (RealStar Influenza S&T RT-PCR Kit 3.0, Altona Diagnostics) to an automated assay with random access (Xpert Flu assay, Cepheid). Automation and random access allowed 7 day/week testing of specimens as they arrived rather than once daily testing 3 days per week without increasing technologist workload. Results. We identified 57 and 68 confirmed cases of influenza in 2012/13 and 2013/14 (Fig 1). Total patient days were similar during the two time periods (66,308 vs. 66,366). TAT was lower in 2013/2014 (35h vs. 3.6h). Daily mean isolation days for all indications (32.9 vs. 27.7, p < 0.001), for contact precautions (25.0 vs. 19.8, p < 0.001) and for droplet precautions (6.0 vs. 3.5, p < 0.001) all fell significantly in 2013/14 while no change was noted in daily mean airborne isolation days (2.6 vs. 2.9, p = 0.20) (Fig 2). Most strikingly, while daily mean droplet precaution days for confirmed influenza rose slightly (0.86 vs. 1.1,p = 0.018), daily mean droplet precaution days for suspected influenza fell 85% (2.7 vs. 0.41, p < 0.001) in 2013/14 (Fig 2). Conclusion. Implementation of an automated test that allowed influenza testing 7 days per week reduced the TAT from days to hours. This resulted in a 42% drop in isolation days for droplet precautions and an 85% reduction in days in droplet precautions for patients with suspected influenza. These results were observed despite an increase in total cases of influenza in 2013/14. Disclosures. All authors: No reported disclosures.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".