Evidence map›Paper›PMID 28207198›Full record

ReviewWiley interdisciplinary reviews. Systems biology and medicine2017

Integrated precision medicine: the role of electronic health records in delivering personalized treatment.

Amy Sitapati, Hyeoneui Kim, Barbara Berkovich, Rebecca Marmor, Siddharth Singh, Robert El-Kareh, Brian Clay, Lucila Ohno-Machado

Abstract readReview
In one paragraph

Review in Wiley interdisciplinary reviews. Systems biology and medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 3 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

27 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. An atomic approach to the design and implementation of a research data warehouse.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Review
  20. User stories as lightweight requirements for agile clinical decision support development.Journal of the American Medical Informatics Association : JAMIA · 2019
    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

8 authors.

Amy SitapatiDepartment of Medicine, UC San Diego, San Diego, CA, USA.
Hyeoneui KimDepartment of Medicine, UC San Diego, San Diego, CA, USA.
Barbara BerkovichUC San Diego Health System, San Diego, CA, USA.
Rebecca MarmorDepartment of Medicine, UC San Diego, San Diego, CA, USA.
Siddharth SinghDepartment of Medicine, UC San Diego, San Diego, CA, USA.
Robert El-KarehDepartment of Medicine, UC San Diego, San Diego, CA, USA.
Brian ClayDepartment of Medicine, UC San Diego, San Diego, CA, USA.
Lucila Ohno-MachadoDepartment of Medicine, UC San Diego, San Diego, CA, USA.

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
BioCADDIE: Biomedical and healthCAre Data Discovery and Indexing Engine centerU24AI117966 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI OHNO-MACHADO, LUCILA · 2014 to 2016
$10.9M
San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
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
Minimizing Physician Errors: Feedback of Patient Outcomes After HandoffsK22LM011435 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI EL-KAREH, ROBERT E · 2013 to 2015
$476k
NCATS NIH HHS UL1 TR001442NHGRI NIH HHS R01 HG008802NIAID NIH HHS U24 AI117966NLM NIH HHS K22 LM011435NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

Precision Medicine involves the delivery of a targeted, personalized treatment for a given patient. By harnessing the power of electronic health records (EHRs), we are increasingly able to practice precision medicine to improve patient outcomes. In this article, we introduce the scientific community at large to important building blocks for personalized treatment, such as terminology standards that are the foundation of the EHR and allow for exchange of health information across systems. We briefly review different types of clinical decision support (CDS) and present the current state of CDS, which is already improving the care patients receive with genetic profile-based tailored recommendations regarding diagnostic and treatment plans. We also report on limitations of current systems, which are slowly beginning to integrate new genomic data into patient records but still present many challenges. Finally, we discuss future directions and how the EHR can evolve to increase the capacity of the healthcare system in delivering Precision Medicine at the point of care. WIREs Syst Biol Med 2017, 9:e1378. doi: 10.1002/wsbm.1378 For further resources related to this article, please visit the WIREs website.

Indexed as

Precision MedicineDecision Support Systems, ClinicalDelivery of Health Care, IntegratedElectronic Health RecordsHealth Information ExchangeHumans

Identifiers

PMID28207198
PMCPMC5400726

What OpenQuestion holds

Textmetadata
LicenceTDM
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.