Transitional care clinics for patients discharged from hospital without a primary care provider: A systematic review
Bibliographic record
Abstract
BACKGROUND: The transition from hospital to home is a high-risk period. Timely follow-up care is essential to reducing avoidable harms such as adverse drug events, yet may be unattainable for patients who lack attachment to a primary care provider. Transitional care clinics (TCCs) have been proposed as a measure to improve health outcomes for patients discharged from hospital without an established provider. In this systematic review, we compared outcomes for unattached patients seen in TCCs after hospital discharge relative to care as usual. METHODS: We searched the following bibliographic databases for articles published on or before August 12, 2022: MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Cochrane Database of Systematic Reviews, PsycINFO, and Web of Science. Five studies were identified that examined the effects of a dedicated postdischarge clinic on emergency department (ED) visits, readmissions, and/or mortality within 90 days of discharge for patients with no attachment to a primary care provider. RESULTS: Studies were heterogeneous in design and quality; all were from urban centers within the United States. Four of the five studies reported a reduction in either the number of ED visits or readmissions in patients seen in a TCC following hospitalization. CONCLUSIONS: TCCs may be effective in reducing hospital contacts in the period following hospital discharge in patients with no established primary care provider. Further studies are required to evaluate the health benefits attributable to the implementation of TCCs across a broad range of practice contexts, as well as the cost implications of this model.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".