Evidence map›Paper›PMID 22320373›Full record

ArticleAcademic emergency medicine : official journal of the Society for Academic Emergency Medicine2012

Electronic versus manual data processing: evaluating the use of electronic health records in out-of-hospital clinical research.

Craig D Newgard, Dana Zive, Jonathan Jui, Cody Weathers, Mohamud Daya

Abstract readComparative StudyEvaluation StudyMulticenter Study
In one paragraph

Article in Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
  2. Article
  3. Database quality assessment in research in paramedicine: a scoping review.Scandinavian journal of trauma, resuscitation and emergency medicine · 2023
    Article
  4. Article
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  6. Article
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  11. Data electronically extracted from the electronic health record require validation.Journal of perinatology : official journal of the California Perinatal Association · 2019
    Article
  12. Priorities to Overcome Barriers Impacting Data Science Application in Emergency Care Research.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2019
    Article
  13. Article
  14. Article
  15. Article
  16. Article
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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.

Craig D NewgardCenter for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, OR, USA. newgardc@ohsu.edu
Dana Zive
Jonathan Jui
Cody Weathers
Mohamud Daya

Funding

OREGON CLINICAL AND TRANSLATIONAL SCIENCE INSTITUTEUL1RR024140 · NCRR · OREGON HEALTH & SCIENCE UNIVERSITY · PI ORWOLL, ERIC S. · 2006 to 2011
$61.1M
Resuscitation Outcomes Consortium (ROC) Regional Clinical Center-PortlandU01HL077873 · NHLBI · OREGON HEALTH & SCIENCE UNIVERSITY · PI DAYA, MOHAMUD RAMZANALI · 2004 to 2015
$3.9M
NCRR NIH HHS UL1 RR024140NHLBI NIH HHS 5-U01-HL077873-01NHLBI NIH HHS U01 HL077873
6 · The paper itself

Abstract

objectivesThe objective was to compare case ascertainment, agreement, validity, and missing values for clinical research data obtained, processed, and linked electronically from electronic health records (EHR) compared to "manual" data processing and record abstraction in a cohort of out-of-hospital trauma patients.

methodsThis was a secondary analysis of two sets of data collected for a prospective, population-based, out-of-hospital trauma cohort evaluated by 10 emergency medical services (EMS) agencies transporting to 16 hospitals, from January 1, 2006, through October 2, 2007. Eighteen clinical, operational, procedural, and outcome variables were collected and processed separately and independently using two parallel data processing strategies by personnel blinded to patients in the other group. The electronic approach included EHR data exports from EMS agencies, reformatting, and probabilistic linkage to outcomes from local trauma registries and state discharge databases. The manual data processing approach included chart matching, data abstraction, and data entry by a trained abstractor. Descriptive statistics, measures of agreement, and validity were used to compare the two approaches to data processing.

resultsDuring the 21-month period, 418 patients underwent both data processing methods and formed the primary cohort. Agreement was good to excellent (kappa = 0.76 to 0.97; intraclass correlation coefficient [ICC] = 0.49 to 0.97), with exact agreement in 67% to 99% of cases and a median difference of zero for all continuous and ordinal variables. The proportions of missing out-of-hospital values were similar between the two approaches, although electronic processing generated more missing outcomes (87 of 418, 21%, 95% confidence interval [CI] = 17% to 25%) than the manual approach (11 of 418, 3%, 95% CI = 1% to 5%). Case ascertainment of eligible injured patients was greater using electronic methods (n = 3,008) compared to manual methods (n = 629).

conclusionsIn this sample of out-of-hospital trauma patients, an all-electronic data processing strategy identified more patients and generated values with good agreement and validity compared to traditional data collection and processing methods.

Indexed as

Biomedical ResearchElectronic Health RecordsEmergency Medical ServicesData CollectionHumansProspective StudiesWashingtonWounds and Injuries

Identifiers

PMID22320373
PMCPMC3278860

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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.