Care transition to skilled nursing facility – A model and a case study
Bibliographic record
Abstract
Background: Healthcare providers have focused on improving patient care transitions to reduce unanticipated readmission costs, improve patient care quality post-discharge and increase patient satisfaction. This is especially true in US since the introduction of the Affordable Care Act. While there are several practices and evidence-based programs discussed in the literature to address care transition post-discharge, the key challenge remains the same – how to structure the care transition program to influence its effectiveness. In this paper, we focus on modeling one particular care transition – moving a patient from a hospital to a skilled nursing facility (SNF) – and discuss how improved capacity building and use of intermediaries such as advanced nurse practitioners have shown promise in reducing patient readmissions.Method: The methodology proposed here uses service dominant (SD) logic research to inductively derive a model for service exchanges between the two provider ecosystems. This model is then used to analyze service gaps and look for opportunities to innovate within an SNF and improve its capacity to deliver care. Use of intermediation that expands the service model with the addition of more care providers besides the hospital and SNF is also discussed to reduce patient readmissions. Results: The study demonstrates that a number of actors have to work collaboratively to make care transition effective in meeting the patient and provider goals. Specifically, when two care facilities, hospital and SNF, are involved in care transition, opportunities exist to improve their internal capacity to address care within and across facilities. Conclusion: The paper makes two important contributions. It shows the role of SD Logic in identifying opportunities for service innovations in support of care transition, and it shows the role of actors in provider-customer ecosystems to make the transition effective.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".