Technology‐enabled integrated care pathway (Tech‐ICP) for behavioral and psychological symptoms of dementia in long‐term care
Bibliographic record
Abstract
BACKGROUND: Majority of long-term care (LTC) residents experience cognitive impairment and behavioral and psychological symptoms of dementia (BPSD). Managing BPSD in LTC is often sub-optimal and variable, resulting in inappropriate use of psychotropic medications and under-utilization of non-pharmacological interventions. We developed and tested an Integrated Care Pathway (ICP) that combines pharmacological and non-pharmacological approaches for BPSD using a measurement based-care approach in select psychiatry inpatient units and LTCs. We now aim to implement and evaluate the ICP combined with technology-based monitoring systems and interventions (Tech-ICP) in LTCs using pragmatic trial design. METHOD: Tech-ICP is a technology-driven solution that integrates assessment tools, monitoring systems, and algorithmic treatment based on ICP principles. It includes dashboards for the clinicians, wearable devices for biometric monitoring, and virtual reality (VR) interventions such as personalized reminiscence therapy and simulation-based experiential learning for staff. We aim to implement the Tech-ICP at 40 LTCs affiliated with academic hospitals across Toronto over three-years in a step wedge design. The implementation will involve three phases, readiness assessment, implementation, and transition to sustainability. We will collect outcomes related to effectiveness of implementation, and site and individual level data related to clinical outcomes. The project will be co-led by persons with lived experience to ensure alignment with priorities of LTC residents and caregivers. RESULT: This study will provide important insights regarding implementation of a clinical intervention for management of BPSD in LTC and its evaluation using pragmatic trial design. It is expected that the Tech-ICP will enhance clinical team capabilities through technology to effectively treat BPSD, reduce inappropriate psychotropic medication use, improve quality of life of LTC residents, and reduce caregiver burden burnout. It is also expected to reduce rate of emergency room visits and hospital admissions for LTC residents. CONCLUSION: Tech-ICP aims to provide a sustainable care model for managing BPSD in LTC's by integrating state of the art technological interventions. This approach is expected to improve care quality by ensuring appropriate use of non-pharmacological and medications, and enhance the overall well-being of both LTC residents and their caregivers. It will also inform design of future pragmatic trials in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".