Understanding health information exchange processes within Canadian <scp>long‐term</scp> care: A scoping review
Bibliographic record
Abstract
BACKGROUND: Providing supportive care to long-term care residents with complex medical conditions generates substantial amounts of health information. This information must be documented, shared and acted upon by the various care providers within the circle of care. OBJECTIVES: The purpose of this scoping review is to describe the current digital health information exchange (HIE) processes used within Canadian long-term care facilities (LTCFs). METHODS: The scoping review followed Arksey and O'Malley's approach to the methodology. Electronic databases (e.g. CINAHL, MEDLINE and SCOPUS) were searched between 2010 and 2020 using terms including 'health information exchange', 'communication' and 'health information technology'. Articles were included if they were Canadian-based and relevant to our definition of health information exchange. RESULTS: The search yielded 2091 citations for title and abstract screening; 78 citations were selected for independent full-text review, 42 of those met study criteria. The findings revealed gaps between the expectations of HIE for quality health care and the realities of HIE processes that impact the provision of care in long-term care. CONCLUSIONS: We conclude that increased provider engagement and effective use of HIE processes is recommended to improve the safety and quality of health care in the long-term care sector. IMPLICATIONS FOR PRACTICE: HIE implementation should be preceded a review of various aspects of workflow to identify information gaps and inefficiencies that can be addressed by digitization.
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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 | 0.040 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.037 | 0.065 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".