Optimizing Health Information Exchange during Patient Transitions into Long-term Care
Bibliographic record
Abstract
Background: Older adults are the highest healthcare users, and their rapidly growing population mounts increasing pressures on the healthcare system, including the demand for long-term care (LTC) beds. Most older adults lose contact with their family physicians on entering LTC as new providers assume responsibility for their care. System fragmentation, including impacts of policies like Bill 7 – permitting temporary placement in LTC facilities nearly 150km away from one’s preferred location – exacerbates this problem. Disruption of care continuity creates challenges for the healthcare workforce and patient care outcomes. This dissertation aims to describe the information exchange activities that occur during primary care to LTC transition, and to explore opportunities to leverage policy to optimize informational continuity during the transition process. Methods: This work includes a three-stage research program comprising a scoping review of the literature pertaining to continuity of care during LTC transition in Canada, followed by a multiple case study design to elicit insights from various LTC providers on the information continuity discourse. The third study was a qualitative descriptive study on family physicians’ perspectives concerning informational continuity practices during LTC transitions. Results: Informational continuity is perceived as a valuable and viable solution to mitigating disrupted relational continuity. However, the information shared currently is inadequate to support informational continuity. Systemic barriers (e.g., document designs, time constraints) and provider perception about the information shared (e.g., redundancy, obsoleteness) contribute to suboptimal information exchange. Health professions education interventions, document revision, the automation of form completion, collaborative documentation practice, warm handoff standards, and efforts to better empower patient families would be needed to optimize informational continuity. Conclusion: Informational continuity remains a promising means to address disrupted continuity. This work calls on policymakers, practitioners, and educators to address practices and systemic issues hindering informational continuity. It encourages further research into digital solutions, stakeholder perspectives, and context-specific continuity frameworks.
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 | 0.027 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".