Peripherally Inserted Central Catheters (PICCs) and Potential Cost Savings and Shortened Bed Stays In an Acute Hospital Setting.
Bibliographic record
Abstract
Peripheral inserted central catheters (PICCs) have increasingly become the mainstay of patients requiring prolonged treatment with antibiotics, transfusions, oncologic IV therapy and total parental nutrition. They may also be used in delivering a number of other medications to patients. In recent years, bed occupancy rates have become hugely pressurized in many hospitals and any potential solutions to free up beds is welcome. Recent introductions of doctor or nurse led intravenous (IV) outpatient based treatment teams has been having a direct effect on early discharge of patients and in some cases avoiding admission completely. The ability to deliver outpatient intravenous treatment is facilitated by the placement of PICCs allowing safe and targeted treatment of patients over a prolonged period of time. We carried out a retrospective study of 2,404 patients referred for PICCs from 2009 to 2015 in a university teaching hospital. There was an exponential increase in the number of PICCs requested from 2011 to 2015 with a 64% increase from 2012 to 2013. The clear increase in demand for PICCs in our institution is directly linked to the advent of outpatient intravenous antibiotic services. In this paper, we assess the impact that the use of PICCs combined with intravenous outpatient treatment may have on cost and hospital bed demand. We advocate that a more widespread implementation of this service throughout Ireland may result in significant cost savings as well as decreasing the number of patients on hospital trollies.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".