Improving Health Care Efficiency Through the Integration of a Physician Assistant into an Infectious Diseases Consult Service at a Large Urban Community Hospital
Bibliographic record
Abstract
BACKGROUND: Physician assistants (PAs) have recently been introduced into the Canadian health care system in some provinces; however, there are little data demonstrating their impact. METHODS: A retrospective case-control study was conducted between January 2010 and December 2013. Length of stay (LOS) and mortality were examined in the infectious diseases consult service (IDCS) compared with hospital-wide controls. The two-year period before the introduction of the PA to the IDCS of a large urban community hospital in Canada (2010 to 2011) was compared with the two-year period following the introduction of the PA (2012 to 2013). RESULTS: Following the introduction of a PA to the IDCS, there was a decrease in time to consultation from 21.4 h to 14.3 h (P<0.0001). LOS was significantly decreased among IDCS patients by 3.6 days more than that seen in matched hospital-wide controls (P=0.0001). Mortality did not significantly change after PA introduction in either cases or controls. DISCUSSION/CONCLUSION: PAs can improve health efficiencies in the Canadian health care setting, leading to reduction in LOS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".