Evidence map›Paper›PMID 25123728›Full record

ReviewYearbook of medical informatics2014

"Big data" and the electronic health record.

M K Ross, W Wei, L Ohno-Machado

Abstract readReview
In one paragraph

Review in Yearbook of medical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed, 5 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

81 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Advancing Alzheimer's research: A review of big data promises.International journal of medical informatics · 2017
    Pooled it
  5. Pooled it
  6. Trial
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Unstructured Electronic Health Records of Dysphagic Patients Analyzed by Large Language Models.IEEE journal of translational engineering in health and medicine · 2025
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review

21 more citing papers are in PubMed but not listed here.

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.

M K RossLucila Ohno-Machado, Division of Biomedical Informatics, 9500 Gilman Drive, MC 0505, La Jolla, California, 92037-0505, USA, Tel: +1 858 822 4931, E-mail: machado@ucsd.edu.
W Wei
L Ohno-Machado

Funding

iDASH: Integrating Data for Analysis, Anonymization, and SharingU54HL108460 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI OHNO-MACHADO, LUCILA · 2010 to 2015
$19.1M
San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
Global Health Informatics for Cancer ResearchD43TW007015 · FIC · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI OHNO-MACHADO, LUCILA · 2004 to 2013
$2.2M
FIC NIH HHS D43 TW007015FIC NIH HHS D43TW007015NCATS NIH HHS UL1TR0001000NHLBI NIH HHS U54 HL108460NHLBI NIH HHS U54HL108460NLM NIH HHS T15 LM011271NLM NIH HHS T15LM011271
6 · The paper itself

Abstract

objectivesImplementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on "big data" in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice.

methodsWe searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to "big data" and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria.

resultsOur final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security.

conclusionThe increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of "big data", and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.

Indexed as

Computational BiologyData MiningElectronic Health RecordsDecision Support Systems, ClinicalMedical Records Systems, ComputerizedNatural Language ProcessingPharmacovigilanceSocial Mediadata miningElectronic health recordsnatural language processingprivacyquality improvementsecurity

Identifiers

PMID25123728
PMCPMC4287068

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.