Effect of Implementation of a Whole Blood Point-of-Care Device on After-Hours Call Backs at a Rural Hospital
Bibliographic record
Abstract
The main advantages of point-of-care testing (POCT) are convenience of testing and immediacy of results. Cost savings are not a usual expectation of POCT. In fact, use of POCT may be several times more expensive in terms of consumables than traditional laboratory testing. However, this cost must also be balanced against potential savings in human resources costs. We present the results of an implementation of the i-STAT POCT device (Abbott Laboratories, Abbott Park, IL) in a small rural laboratory. In the 12 months leading up to this trial, technologists at this laboratory were called back after hours for stat testing requests approximately 60 times per month. Because of union regulations, each call back was remunerated at a minimum of 3 hours of double-time wages, amounting to $227.64 CDN per call back. In this trial, emergency room nursing staff were trained by laboratory staff on how to use the i-STAT, and laboratory technologists were called back only to perform testing not available on the i-STAT. No specific attempt was made to discourage technologist call backs. All tests performed with the i-STAT from the introduction date (October 15, 2010) until March 1, 2011 are included in this evaluation. Detailed cost estimates of i-STAT consumables were recorded for the first 2 months of use and extrapolated to estimate yearly costs. Following the introduction of the i-STAT POC device, there was an immediate and sustained reduction in technologist call backs of approximately 33%, resulting in net cost savings for this small laboratory of approximately $55,500 CDN per year. This device has previously been shown to reduce costs in other settings, although quality assurance must be rigorously maintained as discrepant results have been reported in some settings.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".