Clinical Engineering Review of Large Scale Laboratory Automation in Two Healthcare Settings. Case of NHS Tayside, Dundee, Scotland & Niagara Health, Ontario Canada.
Bibliographic record
Abstract
This comparative study examines approaches to large scale laboratory automation in two different healthcare organizations. It considers the acquisition, implementation and the use of the technology. The two organizations studied, NHS Tayside in Dundee, Scotland and Niagara Health in Niagara, Canada have similar population density, land area and healthcare systems. Each organization implemented laboratory automation systems at around the same time. The study examined laboratory configurations, processes, key performance indicators such as turnaround time, workflow and productivity and identified that the implementation of laboratory automation, together with the use of information technology enabled both laboratories improve their efficiencies. Our research identified key similarities and differences in the organizational approaches to laboratory automation; areas of mutual interest to both organisations were identified where continuing collaboration may be of value moving forward. This is enabled by comparison of key performance indicators, sharing best practices, and enhancing mutual learning.This research shows that both organizations managed to increase productivity and reduce turnaround time through introduction of automation. At the same time, NHS Tayside has taken advantage of technology to deliver additional benefit of increasing the effectiveness of laboratory services in patient care delivery by introducing new diagnostic pathways made possible by automation and associated information technology. Such approaches combined, with developments in the clinical applications of artificial intelligence, provide great future opportunity for Clinical Scientist and Clinical Engineers working together to transform the way modern clinical laboratory operate and impact positively on patient care and outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".