Cost: Benefit of Point-of-Care Blood Gas Analysis vs. Laboratory Measurement During Stabilization Prior to Transport
Bibliographic record
Abstract
INTRODUCTION: This study was conducted to determine whether point-of-care testing, using the iSTAT Portable Clinical Analyzer, would reduce time at the referring hospital required to stabilize ventilated pediatric patients prior to interfacility, air-medical transport. METHODS: The following data were collected prospectively: (1) When a blood gas analysis was ordered; (2) If it was necessary to call in a technician; (3) Waiting time for blood to be drawn; and (4) Waiting time for results. The cost-efficacy of point-of-care testing was calculated based on: (1) Three minutes for a transport team member to draw a sample and obtain a result using the iSTAT (unit cost 8,000 CDN dollars); (2) Lab technician call-back (minimum two hours at 90 dollars); (3) Paramedic overtime (by the minute at 49 dollars/hour); and (4) Cost of charter aircraft wait time (200 dollars per hour) for every hour beyond four hours. RESULTS: Data were collected on 46 ventilated patients over a three month period. A blood gas analysis was ordered on 35 patients. Laboratory technicians were called in for 17 (49%). For 12 (34%) patients, there was a wait for the sample to be drawn, and for 23 (66%), there was a wait for results to become available. Total time waiting to obtain laboratory gases was 526 minutes compared with a calculated 105 minutes using point-of-care testing. An iSTAT cartridge cost of 420 dollars would not have been different from laboratory costs. Cost-saving on technician callback (1,530 dollars), paramedic overtime (690 dollars) and aircraft time waiting charges (2,000 dollars) would have totaled (4,220 dollars). From this study, the cost of point-of-care equipment could be recouped in 101 patients if aircraft charges apply or 192 patients if no aircraft costs are involved. For 11 cases, ventilator adjustments were made subsequently during transport, and for six patients, point-of-care testing, if in place, would have been used to optimize transport care. CONCLUSION: The data from the present study indicate significant cost-efficacy from use of this technology to reduce stabilization times, and support the potential to improve quality of care during air medical interfacility transport.
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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 | 0.001 | 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 | 0.000 |
| 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".