Bluestone’s Emergency Department Early Response Program: Promoting High-Quality and Safe Transitions
Bibliographic record
Abstract
Abstract Almost half of all Assisted Living Community (ALC) residents transfer to the emergency department (ED) each year. For people living with dementia (PLWD), providing a medical history can be a challenge, resulting in prolonged stays and risk of delirium or hospital acquired infections. Bluestone Physician Services developed a ED early response program in which complex care managers (CCM) receive an electronic Admission, Discharge, and Transfer (ADT) notification when one of their patients registers at an ED. If the notification occurs during business hours, the CCMs call the ED using a script and follow-up with a structured fax. We conducted semi-structured interviews with 12 CCMs involved in the program to assess the feasibility and acceptability of the program. There were five themes: patients with dementia and those on hospice were especially likely to benefit from the program; strengths of the program, including increased communication between Bluestone and ED providers; weakness of the program, including a lack of awareness of the program among variable ED staff and challenges with timing the call and fax to maximize benefit. The CCMs also shared some learnings and adaptations that increased contact rate over time and individual success stories. In this session, we will also provide tips on how to integrate real-time ADT notifications into CCM workflows. Next steps include examining the ED provider, patient and caregiver perceptions of the program, quantifying the impact of the program on utilization, and analyzing how the program would perform in other health care settings.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".