Do Post Discharge Phone Calls Improve Care Transitions? A Cluster-Randomized Trial
Bibliographic record
Abstract
IMPORTANCE: The transition from hospital to home can expose patients to adverse events during the post discharge period. Post discharge care including phone calls may provide support for patients returning home but the impact on care transitions is unknown. OBJECTIVE: To examine the effect of a 72-hour post discharge phone call on the patient's transition of care experience. DESIGN: Cluster-randomized control trial. SETTING: Urban, academic medical center. PARTICIPANTS: General medical patients age 18 and older discharged home after hospitalization. MAIN OUTCOMES AND MEASURES: Primary outcome measure was the Care Transition Measure (CTM-3) score, a validated measure of the quality of care transitions. Secondary measures included self-reported adherence to medication and follow up plans, and 30-day composite of emergency department (ED) visits and hospital readmission. RESULTS: 328 patients were included in the study over an 6-month period. 114 (69%) received a post discharge phone call, and 214 of all patients in the study completed the follow outcome survey (65% response rate). A small difference in CTM-3 scores was observed between the intervention and control groups (1.87 points, 95% CI 0.47-3.27, p = 0.01). Self-reported adherence to treatment plans, ED visits, and emergency readmission rates were similar between the two groups (odds ratio 0.57, 95% CI 0.13-2.45, 1.20, 95% CI 0.61-2.37, and 1.18, 95% CI 0.53-2.61, respectively). CONCLUSIONS AND RELEVANCE: A single post discharge phone call had a small impact on the quality of care transitions and no effect on hospital utilization. Higher intensity post discharge support may be required to improve the patient experience upon returning home. TRIAL REGISTRATION: ClinicalTrials.gov NCT01580774.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".