Referral of community-dwelling older adults from eight emergency departments in Canada: A secondary analysis of cross-sectional data from the interRAI multinational emergency department (ED) cohort study
Bibliographic record
Abstract
Background: Emergency Department (ED) overcrowding, unnecessary hospitalization, and alternate level of care has been identified as a major issue in Canada and around the world. This problem especially impacts older adult populations, who are at elevated risk of functional decline and adverse events in hospital-centric systems. This study uses data from the interRAI ED Contact Assessment (ED-CA)[1], a comprehensive geriatric assessment used in the ED, to improve our understanding of ED systems of referral to community resources. Target Audience: ED staff in clinical and leadership positions interested in better understanding community referral processes to improve care for community-dwelling older adults. Methods: This study is a secondary analysis of cross-sectional Canadian data from a cohort study of elderly ED patients. The cohort of community-dwelling patients aged 65 and older presented to the ED between April 2017 and July 2018. They were screened and recruited in 10 EDs across Ontario, Quebec, and Newfoundland. The data were analyzed using frequency and logistic regression analyses, then the results were interpreted in collaboration with two geriatricians and one physiotherapist. What was done: This study explored referral patterns and identified predictors of referral from the ED to five different community health services (occupational therapy, physiotherapy, home care, social work, and/or specialized geriatric services) for supporting community-dwelling older adults. Results: We found that the sample (n=1055) was frail, with high needs and a high risk of ED revisit and institutionalization. Over half of the sample was referred to home care, occupational therapy, and/or physiotherapy, while only 16% were referred to specialized geriatric services. Most patients received multiple referrals from the ED. Province was the most impactful predictor for referral to occupational therapy or physiotherapy (OR for Ontario vs. Quebec=62.12, 95% CI [19.04, 202.70]) and home care (OR for Ontario vs. Quebec=12.09, 95% CI [5.81, 25.17]), while having an unstable condition was the most impactful predictor for social work referral (OR=5.51, 95% CI [3.78, 8.02]) and weight loss was the most impactful predictor for specialized geriatric service referral (OR=8.13, 95% CI [5.49, 12.04]). Other notable predictors included having overwhelmed family members, self-rated health, and having had a fall in the last 90 days. Key Learnings: Occupational therapy, physiotherapy, home care, social work, and specialized geriatric services are distinct services, specialized in addressing a specific set of care needs. We learned that specialized geriatric services are underutilized and may be poorly understood. Next Steps: To improve the quality of ED care, we have provided recommendations for restructuring care processes to promote shifts in clinical culture, evidence-informed decision-making, and proactive referral to community health services. References: Costa A, Hirdes JP, Ariño-Blasco S, Berg K, Boscart V, Carpenter CR, et al. interRAI Emergency Department (ED) Assessment System Manual: For use with the interRAI ED Screener (EDS) and ED Contact Assessment (ED-CA). Version 9.3. Washington, D.C.: interRAI; 2017.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".