A Better Way to Care for Long-Term Care Residents in Times of Medical Urgency: An Implementation Intervention Using a Stepped-Wedge Design to Reduce Unnecessary Acute Care Transfers
Bibliographic record
Abstract
OBJECTIVE: Approximately 25% of long-term care (LTC) residents are transferred to an emergency department (ED) when experiencing an acute change in health status. This can place strain on health care resources and negatively impact residents. Many residents' conditions could be managed within LTC if appropriate supports were provided. This implementation study objective was to optimize and evaluate processes followed when considering acute care management and transfer decisions for residents in LTC. DESIGN: A randomized stepped-wedge design was used to implement a standardized LTC-to-ED care and referral pathway, supported by 2 INTERACT tools. The pathway was implemented within 9 cohorts of (4-5) LTC facilities every 3 months, supported by an implementation coach. Implementation strategies considered local LTC context and barriers, as well as pandemic-related challenges. SETTING AND PARTICIPANTS: 40 LTC facilities and 4 EDs within Calgary, Canada. METHODS: The primary outcome was change in transfers from LTC to ED; secondary outcomes included hospital admissions, use of facilitated telephone consultation between LTC and ED physicians, and community paramedic visits. Analysis used negative binomial regression to estimate the incident rate (per 1000 residents), while adjusting for the different cohorts. An economic evaluation was conducted using a unit cost analysis. RESULTS: A reduction in the incident rate of LTC-to-ED transfers was observed with the intervention (1.70 postintervention vs 1.91 preintervention; P < .001), along with reduction in hospital admission (0.94 vs 1.08; P < .001). There was an increase in use of facilitated telephone consultations between MDs but no increase in community paramedic visits. The intervention saved the health care system CAD$7.9 million over the postimplementation evaluation period. CONCLUSION AND IMPLICATIONS: Implementation of a standardized LTC-to-ED care and referral pathway appears to reduce ED transfers and hospitalizations among LTC residents, while realizing cost savings to the health care system. Reducing unnecessary transfers from LTC to ED, and instead focusing on earlier identification and management of acute medical issues within LTC, looks to be a feasible, patient-centered, and resource-optimized approach to 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".