Electronic Health Record Interventions to Reduce Risk of Hospital Readmissions
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,029 | 0,166 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,009 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».