Exploring the Impact of Embedding Community Resources in the Emergency Department
Bibliographic record
Abstract
Alternate level of Care (ALC) is a pressing issue in the Canadian Healthcare landscape. ALC can be defined as a patient occupying a bed in an acute care facility who no longer requires acute level care. The impacts of ALC are felt throughout the healthcare system. ALC decreases access to acute care beds, adds higher health care costs and can lead to adverse health outcomes for the patient such as nosocomial infections. Persons living with dementia (PLWD) are twice as likely to become ALC once admitted to hospital and remain ALC three times longer than those without dementia. They are often brought to emergency departments for non-acute reasons due to caregiver burnout and the inability to cope with the behavioural and psychological symptoms of dementia. PLWD are admitted at a higher rate from the ED than those without dementia. In an initiative to reduce non-acute hospital admissions of PLWD a pilot project was initiated by the Brantford Brant Norfolk Ontario Health Team. Ontario Health Teams are a health system model introduced in Ontario Canada in 2019. They are comprised of a network of healthcare providers and organizations working together for the delivery of coordinated, effective and efficient health services. Since their inception, the teams have functioned as an agent of change within the health system, identifying gaps, smoothing care transitions and breaking down silos. In the initial pilot project, community resources were embedded in the emergency department (ED) of a local hospital in the Brantford Brant Norfolk region. The aim, to avoid non-acute admissions from the ED of PLWD through community resource education, staff mentorship and behavior management techniques utilizing an integrated team-based model. The team, coined the DREAM Team (Dementia Resource Education Advocacy and Mentorship) consisted of Hospital Navigator, Home and Community Care Coordinator and Dementia Resource Consultant. A retrospective analysis of hospital admission avoidance among 457 PLWD presenting to the ED from April 2021 to June 2023 who interacted with the DREAM team was conducted. Admission avoidance data was manually collected by the hospital navigator. In its first two years the team were able to divert 296 hospital admissions, with an average diversion rate of 64.7%. The admission rate for PLWD decreased during this period from an average of 45.2% (prior two years) to 40.2%. Buoyed by the success of this pilot project, funding was secured to expand the program to 11 additional hospitals in the Ontario Health West Region. An evaluation of the program is on-going. Preliminary data suggest a similar diversion rate amongst additional hospitals and will be further reported. Value to the healthcare system from hospital diversions from the initial pilot project alone amount to over 12.2 million dollars (with 4.0 million in potential ALC prevented) based on average length of stay for PLWD (24 days) and average cost per day. The model has demonstrated many far reaching benefits including low ED return visit rate, decreased ALC and reduced caregiver burnout and stress through resiliency education and respite.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".