Electronic Health Record Interventions to Reduce Risk of Hospital Readmissions
Bibliographic record
Abstract
Importance: Hospital readmissions are associated with significant health care costs and poor patient outcomes. Despite the rapid adoption of electronic health record (EHR) systems, the use of EHR-based interventions to reduce the risk of hospital readmissions is unknown. Objective: To systematically review and estimate the association of EHR-based interventions vs controls with preventing 30-day all-cause hospital readmissions as tested in randomized clinical trials (RCTs). Data Sources: Ovid MEDLINE, Ovid Embase, CINAHL, the Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov were searched from database inception to July 5, 2024, using text words with analogous terms within concept areas of "randomized controlled trial," "hospitalized adults," and "readmissions." Study Selection: RCTs were included if they evaluated the effect of EHR-based interventions on hospital readmissions compared with a control arm without an EHR-embedded component. Studies were excluded if they involved nonhospitalized, pediatric, obstetric, or psychiatric populations or did not report readmission outcomes. Results were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. Data Extraction and Synthesis: Data were extracted independently by 3 reviewers in duplicate. A random-effects model was used to pool data, and the quality of studies was assessed using the Cochrane Risk of Bias tool. Heterogeneity was quantified using the I2 statistic and explored with prespecified subgroup analyses and univariable meta-regression by population demographics, intervention complexity, and publication year. Main Outcomes and Measures: The primary outcome was 30-day all-cause hospital readmission, and other readmission outcomes (eg, unplanned readmissions and readmissions at 3, 6, 12, and 24 months) were examined as secondary outcomes. Results: A total of 116 RCTs involving 204 523 participants (weighted mean [SD] males, 56% [16%]; weighted mean [SD] age, 68 [9] years) were included, with telemonitoring (76 studies [66%]) being the most common EHR-based intervention component followed by case management (45 studies [39%]) and medication reconciliation (33 [28%]). EHR-based interventions were associated with a statistically significant reduction in 30-day all-cause readmissions (OR, 0.83 [95% CI, 0.70-0.99]; I2 = 82%; τ = 0.44 [95% CI, 0.30-0.62]; prediction interval [PI], 0.34-2.06) and 90-day all-cause readmissions (OR, 0.72 [95% CI, 0.54-0.96]; I2 = 78%; τ = 0.34 [95% CI, 0.19-1.00]; PI, 0.33-1.55) compared with control arms. Conclusions and Relevance: In this systematic review and meta-analysis of RCTs, the use of EHR-based interventions was associated with a reduction in 30-day and 90-day hospital readmissions. Future research should examine additional components of EHR interventions to understand and account for remaining gaps in effectiveness.
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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 | 0.029 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".