Implementation of point-of-care blood gas testing at a large community hospital: Cost analysis, sepsis bundle compliance, and employee engagement
Bibliographic record
Abstract
INTRODUCTION/BACKGROUND: Point-of-care testing (POCT) platforms support patient-centered approaches to health care delivery and may improve patient care. We evaluated implementation of a POCT platform at a large, acute care hospital in the Midwestern United States. METHODS: We used lactate testing as part of a sepsis bundle protocol to evaluate compliance and mortality outcomes. Respiratory team members were surveyed to assess perception of efficiency, ease of use, timely patient care, and overall engagement with the POCT system. Annualized cost per test of a benchtop analyzer and a POCT platform were compared across 3 years for each platform. RESULTS: Lactate testing volume increased from 61% to 91%, which was associated with improved sepsis bundle protocol compliance. Employees reported high levels of engagement, improvements in efficiency and time savings, and better patient care with POCT. Average cost per test was $10.02 for the benchtop system and $6.21 for the POCT platform. POCT saved our institution $88,476 annually in labor costs. DISCUSSION: Combined with a robust training program emphasizing the use of lactate testing in the context of the overall clinical picture, POCT enabled adherence to the sepsis bundle protocol and may have contributed to lower mortality. Additionally, the COVID-19 pandemic has provided us with unanticipated benefits of using POCT; it has enhanced our ability to deal with stringent infectious disease protocols, saving time and minimizing patient and staff exposure. CONCLUSIONS: Implementation of a POCT platform was associated with improved compliance to our sepsis protocol, reduced sepsis mortality, high employee engagement, and cost savings.
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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 | 0.001 |
| 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".