A Program to Reduce Emergency Department Transfers and Build Long-Term Care Home Capacity: A Mixed-Methods Study
Bibliographic record
Abstract
OBJECTIVES: Transfers to acute care hospitals expose long-term care residents to potential harm. We implemented Long-Term Care Plus (LTC+) at the outset of the COVID-19 pandemic to reduce emergency department (ED) transfers and improve access to urgent medical services by providing virtual specialist consultation, system navigation, and diagnostic and laboratory testing to 54 long-term care homes (LTCHs). DESIGN: This mixed-methods study aimed to determine if LTC+ led to a decrease in avoidable acute care transfers and to explore participants' perceptions and contextual factors influencing uptake. SETTING AND PARTICIPANTS: LTC+ was implemented across 54 LTCHs and 3 hospital hubs in Toronto, Canada. METHODS: Statistical process control charts were created to detect changes in ED transfer rates, stratifying data into high- and low-uptake LTCHs to evaluate the effect of LTC+ on ED transfer rates across 54 LTCHs. Semi-structured interviews were conducted with health care providers, administrators, residents, and caregivers across 6 LTCHs and 3 hospital hubs and analyzed thematically. RESULTS: There were 9658 ED transfers during the study period (April 2020 to March 2022), of which 3860 (40.0%) did not require admission. LTC+ delivered 534 virtual consultations, with 5 LTCHs accounting for 59% of program use. Compared with baseline (January 2019 to February 2020), transfer rates decreased by 40%, with no difference seen between LTCHs with high vs low uptake. Factors influencing uptake include program awareness, motivation, alignment of LTCH resources and program services, and commitment to ED avoidance. CONCLUSIONS AND IMPLICATIONS: The LTC+ program did not reduce ED transfers beyond secular trends attributable to the broader effects of the COVID-19 pandemic. Participants that used LTC+ identified important benefits that extended beyond ED avoidance including building self-efficacy and capacity in LTCHs to provide client-centered care with cross-sectoral collaboration. Refinements to the LTC+ program design and delivery and structural changes are needed to increase impact.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".