Exploring Innovations and Factors to Optimize Adult Neurosurgery Inpatient Flow in Alberta
Bibliographic record
Abstract
ABSTRACT Background: Poorly managed inpatient flow can lead to adverse health outcomes, including increased mortality and readmission rates. In neurosurgery, optimizing inpatient flow is crucial to improving patient experience and outcomes, but the factors influencing it are unclear. A preliminary analysis revealed suboptimal average length of stay (ALOS) and expected length of stay (ELOS) rates – key metrics used to assess inpatient flow – across Alberta, Canada. The purpose of this study was to evaluate the current state of inpatient flow in Alberta’s neurosurgical care and explore strategies for enhancement. Methods: This study used mixed methods: a rapid scoping review and a retrospective cohort study. The rapid scoping review synthesized peer-reviewed and gray literature (after a three-stage screening process) to identify factors impacting neurosurgery inpatient flow across jurisdictions. The cohort study analyzed Alberta’s adult neurosurgical patient data from 2009 to 2019 to explore how patient- and system-level factors relate to ALOS/ELOS rates. Results: Nine of the 391 screened articles were included in the review. Three main themes emerged influencing neurosurgery inpatient flow: interdisciplinary care pathways, introducing new roles and identification of risk factors. Building on these themes, patient- and system-level factors impacting ALOS/ELOS were explored. ALOS/ELOS rates varied among the five Alberta Health Services zones, with Rural Zone 1 having the highest and significantly different rate. Age, sex, zone and comorbidities significantly accounted for differences in ALOS/ELOS rates ( p < 0.001). Conclusions: Neurosurgery patients in Alberta are experiencing longer hospital stays than expected. Several areas requiring further research have been identified, along with potential strategies to enhance patient care and outcomes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".