Evidence mapPaperPMID 40674049Full record

SynthesisJAMA network open2025

Electronic Health Record Interventions to Reduce Risk of Hospital Readmissions: A Systematic Review and Meta-Analysis.

Badal S B Pattar, Abigail Ackroyd, Emir Sevinc, Taylor Hecker, Keila Turino Miranda, Caitlin McClurg, Kyle Weekes, Matthew T James, Neesh Pannu, Pietro Ravani and 3 more

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Risk of Hospital Readmission among People with CKD: A Population-Based Cohort Study.Clinical journal of the American Society of Nephrology : CJASN · 2026
    Article
  2. Hospital Readmissions in People with CKD: Moving a Stubborn Needle.Clinical journal of the American Society of Nephrology : CJASN · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Badal S B PattarDepartment of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Abigail AckroydDepartment of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Emir SevincDepartment of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Taylor HeckerDepartment of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Keila Turino MirandaCardiovascular Health and Autonomic Regulation Laboratory, Department of Kinesiology and Physical Education, McGill University, Montreal, Quebec, Canada.
Caitlin McClurgLibraries and Cultural Resources, University of Calgary, Calgary, Alberta, Canada.
Kyle WeekesDepartment of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Matthew T JamesDepartment of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Neesh PannuDepartment of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Pietro RavaniDepartment of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Paul E RonksleyO'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada.
Sofia B AhmedDepartment of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Tyrone G HarrisonDepartment of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

Funding

CIHR
6 · The paper itself

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.

Indexed as

Electronic Health RecordsPatient ReadmissionFemaleHumansMaleRandomized Controlled Trials as Topic

Identifiers

PMID40674049
PMCPMC12272288

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.