Evidence map›Paper›PMID 29741693›Full record

ReviewHuman molecular genetics2018

Genomics and electronic health record systems.

Lucila Ohno-Machado, Jihoon Kim, Rodney A Gabriel, Grace M Kuo, Michael A Hogarth

Abstract readReview
In one paragraph

Review in Human molecular genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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

  1. A Systematic Review of Network Studies Based on Administrative Health Data.International journal of environmental research and public health · 2020
    Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Introducing HL7 FHIR Genomics Operations: a developer-friendly approach to genomics-EHR integration.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
  10. Article
  11. Development and implementation of an electronic medical record module to track genetic testing results.Genetics in medicine : official journal of the American College of Medical Genetics · 2021
    Article
  12. Article
  13. Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. 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

5 authors.

Lucila Ohno-MachadoUCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
Jihoon KimUCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
Rodney A GabrielUCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
Grace M KuoSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
Michael A HogarthUCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.

Funding

Protecting Privacy and Facilitating Shared Access of Clinical and Genetic Data of Special PopulationsR01HL136835 · NHLBI · YALE UNIVERSITY · PI OHNO-MACHADO, LUCILA · 2018 to 2022
$6.5M
Decentralized differentially-private methods for dynamic data release and analysisR01GM118609 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN, OHNO-MACHADO, LUCILA · 2017 to 2020
$2.3M
iCONCUR: informed CONsent for Clinical data and biosample Use for ResearchR01HG008802 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN, KIM, HYEON-EUI · 2015 to 2017
$1.3M
NHGRI NIH HHS R01 HG008802NHLBI NIH HHS R01 HL136835NIGMS NIH HHS R01 GM118609
6 · The paper itself

Abstract

Several reviews and case reports have described how information derived from the analysis of genomes are currently included in electronic health records (EHRs) for the purposes of supporting clinical decisions. Since the introduction of this new type of information in EHRs is relatively new (for instance, the widespread adoption of EHRs in the United States is just about a decade old), it is not surprising that a myriad of approaches has been attempted, with various degrees of success. EHR systems undergo much customization to fit the needs of health systems; these approaches have been varied and not always generalizable. The intent of this article is to present a high-level view of these approaches, emphasizing the functionality that they are trying to achieve, and not to advocate for specific solutions, which may become obsolete soon after this review is published. We start by broadly defining the end goal of including genomics in EHRs for healthcare and then explaining the various sources of information that need to be linked to arrive at a clinically actionable genomics analysis using a pharmacogenomics example. In addition, we include discussions on open issues and a vision for the next generation systems that integrate whole genome sequencing and EHRs in a seamless fashion.

Indexed as

Big DataElectronic Health RecordsGenome, HumanGenomicsHumansPharmacogenetics

Identifiers

PMID29741693
PMCPMC5946823

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.