Use of Lean Response to Improve Pandemic Influenza Surge in Public Health Laboratories
Bibliographic record
Abstract
A novel infl uenza A (H1N1) virus detected in April 2009 rapidly spread around the world.North American provincial and state laboratories have well-defi ned roles and responsibilities, including providing accurate, timely test results for patients and information for regional public health and other decision makers.We used the multidisciplinary response and rapid implementation of process changes based on Lean methods at the provincial public health laboratory in British Columbia, Canada, to improve laboratory surge capacity in the 2009 infl uenza pandemic.Observed and computer simulating evaluation results from rapid processes changes showed that use of Lean tools successfully expanded surge capacity, which enabled response to the 10-fold increase in testing demands. A novel infl uenza A (H1N1) virus was detected inMexico and the southwestern United States in early April 2009 (1).Within days after confi rmation that this virus was circulating in the western Canadian province of British Columbia, the number of requests for infl uenza diagnostic tests rapidly increased.It became evident that current operations would not enable the British Columbia Public Health Microbiology & Reference Laboratory (PHMRL), the major provider of infl uenza diagnosis for this province, to meet testing demands.We describe Lean processes that were implemented to rapidly expand surge capacity. Methods Prepandemic Testing for Infl uenzaThe PHMRL serves the entire health care system for the western Canadian province of British Columbia (population 4.45 million).Pandemic planning lead by the Canadian Public Health Laboratory Network included implementation of a reverse transcription PCR (RT-PCR) platform.Before the pandemic, sample data were entered into the Laboratory Information System (LIS) and barcoded in the Central Processing & Receiving section; the accessioned respiratory samples were then transported to the Virology Laboratory, located 3 fl oors away.In the Virology Laboratory, 1 laboratory assistant organized the samples and transferred aliquots into labeled tubes.Testing was conducted by 1 medical laboratory technologist; tasks included nucleic acid extraction, RT-PCR, analysis of results, and report of results into the LIS.One easyMag extractor (bioMérieux, Marcy l'Etoile, France) (capacity 22 patient samples) and 1 ABI 7900 RT-PCR machine (Applied Biosystems, Foster City, CA, USA) (capacity 92 patient samples) were used.These processes were conducted 10.5 h/d, 6 d/wk during the normal British Columbia infl uenza season (September-March) by 1 laboratory assistant and 2 medical laboratory technologists (1 technologist on each of 2 shifts).These assignments enabled PHMRL to meet prepandemic demand for infl uenza testing.Test results were available on the same day as arrival in PHMRL, except for weekends.Volumes seldom exceeded 50 samples/d.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".