The physician assistant hospitalist: a time-motion study
Bibliographic record
Abstract
Objective: The role of hospital-based physician assistants (PAs) is in need of delineation. To learn more about their activities, an administrative research project compared the tasks of hospitalists. In this setting an MD-PA team managed the adult medicine ward each weekday while three MDs rotated shifts.Methods: A priori a survey of hospitalist activities was administered to four providers in a medium sized hospital (3 MDs and 1 PA). This was followed by time-motion documentation that involved shadowing each member of the MD-PA hospitalist teams over a three-month period. A univariate analysis of activity patterns (perceived and observed) assessed what was perceived and what actually occurred on the wards. The mean, standard deviation, and difference in means for each task were calculated.Results: In the survey the PA reported she spent one-half of her hospitalist workdays on direct patient care and the physicians spent less time on direct patient care. Physicians believed they spent half days on direct patient care and believed the PA spent less time on direct patient care than they did. In the time-motion study shadowing the four hospitalists separately what was observed was that the PA spent 18% of her workday on direct patient care and 54% on indirect patient care - primarily patient encounter documentation. The three physicians spent 15% of their workday on direct patient care and 54% on indirect patient care – primarily patient encounter documentation. In summary, the perception of what each provider thought they did and what they in fact did differed significantly when actually observed. All four hospitalists (regardless of team composition) spent less than 20% of their workday with patients, and the rest divided among documentation, examination and test results, hospital meetings, and breaks. Task activity was similar for all providers except MDs attended more administrative meetings than the PA.Conclusions: The perception that physicians have of PA roles and what a PA actually does has been a reoccurring observation. A lack of understanding of PA role delineation by physicians may contribute to employment reluctance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".