Feasibility of Care Coordination to Reduce Unnecessary Hospitalization For Assisted Living Residents
Bibliographic record
Abstract
Abstract Almost half of Assisted Living Community (ALC) residents visit the emergency department (ED) yearly, experiencing more visits and longer stays compared to community-dwelling older adults. People living with Alzheimer’s disease and related dementias (ADRD) are at greater risk for delirium, falls, and accelerated decline associated with increased ED visits. Each transition provides an opportunity for care coordination and avoidance of unnecessary hospital admission. Bluestone Accountable Care Organization developed ED Early Response, a care coordination program. Care managers provide timely, structured information to ED providers via phone and fax within 120 minutes of ED registration. We assessed the feasibility of, and adherence to, the program. Between November 2023 and June 2024, we enrolled 1,376 patients with 1,989 eligible ED visits (mean: 1.4 visits per patient), 1,237 ED visits for patients with ADRD and 752 ED visits for patients without ADRD. Qualifying visits occurred during working hours (8 a.m. - 4 p.m.), with 82.5% of visits (n = 1,641) identified via electronic admission, discharge, and transfer notifications, and 17.5% of visits (n = 348) identified through direct communication between ALCs and care managers. Care managers successfully provided real-time information to ED providers for 44% of the eligible ED visits (547 of 1,237) for patients with ADRD, and for 40% of visits (304 of 752) for patients without ADRD. Care managers self-reported a hospital avoidance rate of 11%. While adherence was lower than anticipated, early structured communication could reduce unnecessary hospital admissions for ALC residents.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".