Implementation and adoption of advanced care planning in the elderly trauma patient
Bibliographic record
Abstract
Background: Geriatric trauma has high morbidity and mortality, often requiring extensive hospital stays and interventions. The number of geriatric trauma patients is also increasing significantly and accounts for a large proportion of trauma care. Specific geriatric trauma protocols exist to improve care for this complex patient population, who often have various comorbidities, pre-existing medications, and extensive injury within a trauma perspective. These guidelines for geriatric trauma care often suggest early advanced care planning (ACP) discussions and documentation to guide patient and family-centered care. Methods: A provincial ACP program was implemented in April of 2012, which has since been used by our level 1 trauma center. We applied a before and after study design to assess the documentation of goals of care in elderly trauma patients following implementation of the standardized provincial ACP tool on April 1, 2012. Results: Documentation of ACP in elderly major trauma patients following the implementation of this tool increased significantly from 16 to 35%. Additionally, secondary outcomes demonstrated that many more patients received goals of care documentation within 24 h of admission, and 93% of patients had goals of care documented prior to intensive care unit (ICU) admission. The number of trauma patients that were admitted to the ICU also decreased from 17 to 5%. Conclusion: Early advanced care planning is crucial for geriatric trauma patients to improve patient and family-centered care. Here, we have outlined our approach with modest improvements in goals of care documentation for our geriatric population at a level 1 trauma center. We also outline the benefits and drawbacks of this approach and identify the areas for improvement to support improved patient-centered care for the injured geriatric patient. Here, we have provided a framework for others to implement and further develop.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".