Impact of Physician Navigators on productivity indicators in the ED
Bibliographic record
Abstract
OBJECTIVES: We created Physician Navigators in our ED to help improve emergency physician (EP) productivity. We aimed to quantify the effect of Physician Navigators on measures of EP productivity: patient seen per hour (Pt/hr), and turn-around time (TAT) to discharge. Secondary objectives included examining their impact on measures of ED throughput for non-resuscitative patients: ED length of stay (LOS), door-to-physician time and left-without-being-seen rates (LWBS). METHODS: In this retrospective study, 6845 clinical shifts worked by 20 EPs at a community ED in Newmarket, Canada from 1 January 2012 to 31 March 2015 were evaluated. Using a clustered design, we compared productivity measures between shifts with and without Physician Navigators, by physician. We used a linear mixed model to examine mean changes in Pt/hr and TAT to discharge for EPs who employed Physician Navigators. For secondary objectives, autoregressive modelling was performed to compare ED throughput metrics before and after the implementation of Physician Navigators for non-resuscitative patients. RESULTS: Patient volumes increased by 20 patients per day (p<0.001). Mean Pt/hr increased by 1.07 patients per hour (0.98 to 1.16, p<0.001). The mean TAT to discharge decreased by 10.6 min (-13.2 to -8.0, p<0.001). After implementation of the Physician Navigator programme, overall mean LOS for non-resuscitative patients decreased by 2.6 min (p=0.007), and mean door-to-physician time decreased by 7.4 min (p<0.001). LBWS rates decreased from 1.13% to 0.63% of daily patient volume (p<0.001). CONCLUSION: Despite an ED volume increase, the use of a Physician Navigator was associated with significant improvements in EP productivity, and significant reductions in ED throughput times.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".