Transitional care clinics: An innovative approach to reducing readmissions, optimizing outcomes and improving the patient’s experience of discharge care-conducting a feasibility study
Bibliographic record
Abstract
Rehospitalization, which is defined as a return to the hospital for the same or related care within 30 days, is often avoidable and adds significant costs to health care spending. A growing body of evidence indicates that when patients move from one health care setting to another, a period known as the care transition, timely access to follow–up care is one of the keys to avoiding unplanned readmissions. Of late, transitional care clinics (TCCs), sometimes referred to as post-discharge clinics, have emerged as an innovative approach to readmission reduction. TCCs bridge the care gap between hospital discharge and post-acute care follow-up by providing access to care for patients with fragmented care or at high risk for readmission. These clinics are typically hospital-based and nurse practitioner-led which makes sense in terms of both cost-containment and quality of care. They provide an alternative to the use of emergency services, improve workflow for referring physicians, and support care navigation back to community providers. TCCs support the discharge plan of care and long-term outcomes by providing an intense focus on patient education, disease and medication self-management, and coordination of care. By providing access to care and improving communication across the continuum of care, TCCs can improve the quality of the care transition and reduce avoidable readmissions. A feasibility analysis can help to provide answers and guide project planning. This article describes how to conduct such a feasibility analysis of establishing a TCC as well as the organizational, financial and market factors impacting the feasibility of establishing such a clinic.
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 | 0.047 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".