Effectiveness of Postdischarge Telephone Calls in Reducing Hospital Utilization: Quasi-Randomized Controlled Trial
Bibliographic record
Abstract
Background: Unplanned emergency department (ED) visits and hospital readmissions following discharge contribute to patient distress, increased health care costs, and system inefficiencies. Early postdischarge follow-up can improve care transitions, yet evidence on the effectiveness of telephone-based interventions remains mixed. Telephone calls, a low-barrier form of digital health, may enhance equity and accessibility by reaching patients who face challenges with in-person or higher-technology follow-up. Objective: This study evaluated the impact of a nurse-led postdischarge telephone intervention delivered by Fraser Health Virtual Care on short-term ED visits and hospital readmissions among recently discharged high-risk patients. Secondary objectives included examining patient experiences with the service and identifying care gaps addressed during follow-up calls. Methods: A pragmatic quasi-randomized trial was conducted (May 2022-September 2022). Participants were eligible if they were aged 18 years or older and classified as high-risk for readmission using the LACE (Length of stay, Acuity of admission, Comorbidities, and Emergency department use) index (≥10 or <9 and ≥45 y). Participants were allocated to either a postdischarge telephone intervention group or a standard care control group based on daily nurse availability. Intervention participants received a structured nurse-led call 48 hours after discharge assessing understanding of discharge instructions, medication management, follow-up appointments, and home supports. Primary outcomes were ED visits within 7 and 30 days post call; secondary outcomes were hospital readmissions and patient experience. Negative binomial regression models were used to calculate adjusted incident rate ratios (IRRs). Results: A total of 7091 participants were included (intervention: n=3911, of whom 1752 completed the call; control: n=3180). Postdischarge calls significantly reduced ED visits at both 7 days (adjusted IRR 0.719, 95% CI 0.617-0.837; P<.001) and 30 days (IRR 0.878, 95% CI 0.783-0.983; P=.02). No statistically significant reductions were observed in hospital readmissions at either 7 days (IRR 0.809; P=.13) or 30 days (IRR 0.942; P=.54). Forty percent of completed calls (n=701) identified at least 1 gap in discharge understanding or follow-up care. Most participants found the calls helpful and reported increased confidence in managing their care. Conclusions: Structured nurse-led postdischarge telephone calls significantly reduced short-term ED utilization but did not impact readmission rates. These findings support the role of telephone-based virtual care as a scalable, low-barrier strategy to improve care transitions and reduce avoidable ED visits. Additional or ongoing interventions may be required to influence hospital readmission outcomes among high-risk patients.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".