Improving Continuity of Care Reduces Emergency Department Visits by Long-Term Care Residents
Bibliographic record
Abstract
INTRODUCTION: Care by Design™ (CBD) (Canada), a model of coordinated team-based primary care, was implemented in long-term care facilities (LTCFs) in Halifax, Nova Scotia, Canada, to improve access to and continuity of primary care and to reduce high rates of transfers to emergency departments (EDs). METHODS: This was an observational time series before and after the implementation of CBD (Canada). Participants are LTCF residents with 911 Emergency Health Services calls from 10 LTCFs, representing 1424 beds. Data were abstracted from LTCF charts and Emergency Health Services databases. The primary outcome was ambulance transports from LTCFs to EDs. Secondary outcomes included access (primary care physician notes in charts) and continuity (physician numbers and contacts). RESULTS: After implementation of CBD (Canada), transports from LTCFs to EDs were reduced by 36%, from 68 to 44 per month (P = .01). Relational and informational continuity of care improved with resident charts with ≥10 physician notes, increasing 38% before CBD to 55% after CBD (P = .003), and the median number of chart notes increased from 7 to 10 (P = .0026). Physicians contacted before 911 calls and onsite assessment increased from 38% to 54% (P = .01) and 3.7% to 9.2% (P = .03), respectively, before CBD to after CBD. CONCLUSION: A 34% reduction in overall transports from LTCFs to EDs is likely attributable to improved onsite primary care, with consistent physician and team engagement and improvements in continuity of care.
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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 | 0.009 |
| 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 | 0.000 |
| 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".