Depiction of a Novel Patient Navigator Program to Support Delayed Discharges Among Older Adults Admitted to Acute Care
Bibliographic record
Abstract
Background A novel Patient Navigator Program (PNP) was introduced at a Canadian hospital’s Reactivation Care Centre (RCC) to support transitions by helping older adults navigate the complexities of delayed discharge stays by improving their transition from hospital to home. The PNP was comprised of a community agency patient navigator who was embedded into the RCC setting to support transitions in care, and who followed patients up to 90 days post-hospital discharge. The purpose of this study was to describe the PNP, which included detailing the needs of patients (i.e., socio-demographics, case-mix, delayed discharge), the scope of service provision (i.e., referral process, follow-up duration), and patient outcomes (i.e., post-discharge location). Methods A cohort observational design was used to collect data on the PNP mainly via the patient navigator’s clinical tracking sheet, and secondly via the hospital’s administrative system. Data analysis included the use of frequencies and descriptive statistics. Results Between November 2021 and October 2022, 100 patients were referred to the PNP, with 70 patients (39% male; 61% female; median age of 81 years) being admitted to the patient navigator’s caseload. The patient navigator provided follow-up care for a median of 58 days, and supported 76% of the patients (n=53) to return to their next point of care (e.g., homes or to a supportive housing setting). Conclusion The PNP led to a high proportion of patients being discharged back to the community. This study provides insights to providers and decision-makers interested in implementing PNP care models in a hospital in partnership with a community agency.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".