Describing and Predicting Trajectories of Healthcare Utilization Among Older Adults Presenting to an Emergency Department Using the interRAI Emergency Department Screener
Bibliographic record
Abstract
Introduction: Although older adults visit emergency departments (EDs) more than any other age group, the trajectories of healthcare utilization older adults experience post-ED are not well described. Further, whether rapid ED assessment tools can predict trajectories and discharge destinations remains unclear. Methods: Older adults (≥65 years) who presented to an ED at a large Canadian urban academic hospital were recruited (January 2018-April 2019). The interRAI ED Screener (EDS) was completed on presentation. Patients were categorized by EDS risk score (1/2=low, 3/4=moderate, 5/6=high) and had their discharge destinations tracked. Patients admitted to hospital were tracked until their final discharge destination. Crude and age/sex-adjusted odds ratios and c-statistics were obtained to examine associations between EDS scores and discharge destinations. Results: Of 751 patients (mean/SD age 77.68/8.43; 41.3% male), 200/26.6% had a high-risk EDS score. 58.3% were discharged home, 39.7% were admitted to hospital, and 2.0% were discharged to rehabilitation/long-term care (LTC) settings directly from the ED. The high-risk group had lower odds of home discharge (aOR=0.47, 95%CI 0.31-0.71, ppp=0.038) and have a geriatrician consulted (aOR=3.72, 1.17-11.86, p=0.026). The EDS had poor prediction of post-ED hospitalization (C-statistic=0.58, 95%CI 0.54-0.62), but reasonable prediction of post-ED LTC home/rehabilitation centre admission (0.75, 0.63-0.87), albeit the number of these outcomes were small (n=15). Conclusions: We describe a range of healthcare trajectories older adults experience following ED presentation. Stratification by EDS risk groups could help to proactively identify the need for geriatric consultation earlier and resource utilization trajectories after an index ED visit, which could better enable the planning and organization of acute healthcare services for older adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".