Evidence map›Paper›PMID 36434202›Full record

ReviewPediatric research2023

Utilizing big data from electronic health records in pediatric clinical care.

Charles G Macias, Kenneth E Remy, Amie J Barda

Abstract readReview
In one paragraph

Review in Pediatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.Health care analysis : HCA : journal of health philosophy and policy · 2025
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Federated data health networks hold potential for accelerating emergency research.Journal of the American College of Emergency Physicians open · 2023
    Article
  10. Article
  11. Artificial intelligence: Revolutionizing pediatric emergency care - A narrative review.International journal of critical illness and injury science
    Review
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

3 authors.

Charles G MaciasDepartment of Pediatrics, Division of Pediatric Emergency Medicine, Rainbow Babies and Children's Hospital, Case Western Reserve University, Cleveland, OH, USA. Charles.Macias@uhhospitals.org.
Kenneth E RemyDepartment of Pediatrics, Division of Pediatric Critical Care Medicine, Rainbow Babies and Children's Hospital, Cleveland, OH, USA.
Amie J BardaDepartment of Population and Quantitative Health Sciences, Case Western Reserve, University School of Medicine, Cleveland, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Big data has the capacity to transform both pediatric healthcare delivery and research, but its potential has yet to be fully realized. Curation of large multi-institutional datasets of high-quality data has allowed for significant advances in the timeliness of quality improvement efforts. Improved access to large datasets and computational power have also paved the way for the development of high-performing, data-driven decision support tools and precision medicine approaches. However, implementation of these approaches and tools into pediatric practice has been hindered by challenges in our ability to adequately capture the heterogeneity of the pediatric population as well as the nuanced complexities of pediatric diseases such as sepsis. Moreover, there are large gaps in knowledge and definitive evidence demonstrating the utility, usability, and effectiveness of these types of tools in pediatric practice, which presents significant challenges to provider willingness to leverage these solutions. The next wave of transformation for pediatric healthcare delivery and research through big data and sophisticated analytics will require focusing efforts on strategies to overcome cultural barriers to adoption and acceptance. IMPACT: Big data from EHRs can be used to drive improvement in pediatric clinical care. Clinical decision support, artificial intelligence, machine learning, and precision medicine can transform pediatric care using big data from the EHR. This article provides a review of barriers and enablers for the effective use of data analytics in pediatric clinical care using pediatric sepsis as a use case. The impact of this review is that it will inform influencers of pediatric care about the importance of current trends in data analytics and its use in improving outcomes of care through EHR-based strategies.

Indexed as

Big DataSepsisArtificial IntelligenceChildElectronic Health RecordsHumansMachine Learning

Identifiers

PMID36434202
PMCPMC9702658

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.