Reducing potentially avoidable acute care transfers from long-term care homes: Developing a taxonomy of interventions and improving approaches to evaluate intervention effectiveness
Bibliographic record
Abstract
Potentially avoidable emergency department (ED) transfers and hospitalizations from long-term care (LTC) homes, providing 24-hour nursing care, represent an important quality of care challenge. These events are defined as those stemming from clinical conditions that theoretically could be managed onsite with appropriate primary care. They may occur contrary to residents’ advance directives, expose residents to serious adverse events, and represent inefficiencies in healthcare systems. There are important limitations of research investigating interventions aimed at reducing transfers from LTC. These limitations make it challenging to adapt any proposed intervention to the needs and preferences of transfer decision-makers, mainly, primary care physicians, other front-line staff, LTC residents and their family members. The primary aim of this dissertation was to conduct a series of methodological and substantive substudies to advance knowledge about potentially avoidable acute care transfers from LTC homes and the interventions aimed at their reduction. The secondary aim was to inform the design of future studies such that they can assess the impact of an exposure of interest that exists under regular conditions (non-experimental) on reducing a meaningful and contextually relevant outcome using causal inference methods. In the first manuscript, I addressed the challenges related to the complexity of interventions that aimed at reducing ED transfers and/or hospitalizations among LTC residents experiencing an acute change in their health. Given the inconsistencies and confusion in the literature regarding intervention terminology, I conducted a systematic scoping review to propose a cohesive taxonomy of such interventions. In synthesizing 90 studies, I identified six intervention categories (e.g., advance care planning, transitional care), and four intervention components (i.e., human resources, training, technology, tools). In the second manuscript, I tackled the shortcomings in the literature surrounding measurement of acute care transfers from LTC. Using real-world data pertaining to a sample of Quebec LTC residents who received care in a tertiary hospital ED, I measured proportions of potentially avoidable ED transfers and hospitalizations associated with conditions manageable onsite and compared these proportions with those reported for the rest of Canada. A total of 1,233 transfers by 692 residents were recorded, among which 36.3% were classified as being potentially avoidable. Potentially avoidable ED transfers with or without hospitalizations accounted for 95% of potentially avoidable transfers, and hence, were identified as an important LTC quality measure. Proportions of all outcomes in Quebec were comparable to those from the rest of Canada.In the third manuscript, using acute care transfers from the LTC setting as a motivating example, I illustrated the usefulness of conceptualizing a causal diagram that encodes known or suspected associations between measured and unmeasured factors, the exposure of interest (advance care planning) and the primary study outcome (potentially avoidable ED transfers). I demonstrated how encoded information representing realistic study scenarios can be used to design and implement the Monte-Carlo simulation analyses using standard statistical software for repeated simulation. This dissertation has implications for future research, clinical practice, and primary care policy-making. Findings provide important insights into proactive models of person-centred care in LTC homes. The proposed taxonomy of interventions can inform successful intervention designs and allow to draw meaningful conclusions about their effectiveness/efficacy in future literature reviews which would be necessary for eventual policy change. The results from these three dissertation manuscripts will inform future observational studies, including that of my research group which plans to conduct further investigation
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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 | 0.199 | 0.326 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.022 | 0.014 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| 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".