The InterRAI ED tool for screening older patients in the emergency department: “What am I supposed to do with this?”
Bibliographic record
Abstract
in Toronto describe the use of the interRAI Emergency Department screener in predicting the trajectories of health care utilization among older patients who presented to the emergency department (ED) in Toronto. 1 Their goal was to determine if the rapid screening tool would be able to predict the health care utilization of older patients seen in the ED.A smartphone app was used by the triage nurse during the presentation of the patient, among a convenience sample.In short, the app was designed to define if an older patient had challenges in basic self-care (basic activities of daily living), cognition, caregiver burden, self-reported health, stability of prior conditions, dyspnea, and depression.The answers to the questions resulted in a low, medium and high risk of additional health care utilization.The authors mapped the trajectories of 755 older patients after their emergency department care.About 40% of the patients were hospitalized after their ED care.A quarter of the 755 patients were identified as high-risk at the time of their triage.Those with a high-risk score on the interRAI were more likely to be admitted to the hospital from the ED, more likely to stay longer in the hospital and receive a geriatric consultation.The tool was not helpful in identifying those who were at high risk of returning to the hospital in 30 days.In a recent discussion, at Advocate Health in Wisconsin, of efforts to improve the care transitions of older patients from the emergency department to home, a nurse posed a straightforward question about the triage tool for which we had been advocating.She asked, "What am I supposed to do with this information?"The emergency nurses and emergency physicians waited for a response from the leaders in the room.Everyone knew that the response would drive the engagement of the nursing staff on further implementation of the screening tool.Our response would determine if the nurses would change their practice and whether their efforts would be followed with improvements in care.Our system was discussing how to use such a tool to improve our ability to identify those older patients who were at highest risk of returning to the emergency department.I wish to capture that moment in our efforts to improve the emergency department care for older patients as I reflect on Dr. Downer and colleagues' paper.I will describe a few caveats of the study and highlight key points we can take from the paper.I will further frame an evidence-based response to the nurse's question and propose some practical steps to consider.First, a few caveats should be noted from the Dr. Matthew Downer et al study.The emergency department which was the setting of the study is a site of best practice in North America for the emergency care of older adults.The systems of care at this site may function better than most
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".