Evidence map›Paper›PMID 30786301›Full record

SynthesisApplied clinical informatics2019

Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review.

Chenchen Feng, David Le, Allison B McCoy

Abstract readSystematic Review
In one paragraph

Synthesis in Applied clinical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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

  1. Ambulatory Medication Safety in Primary Care: A Systematic Review.Journal of the American Board of Family Medicine : JABFM
    Pooled it
  2. Augmenting Electronic Health Records for Adverse Event Detection.medRxiv : the preprint server for health sciences · 2026
    Article
  3. Article
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  8. Malpractice Claims and Incident Reporting: Two Faces of the Same Coin?International journal of environmental research and public health · 2022
    Article
  9. Observational
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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

3 authors.

Chenchen FengTulane University School of Medicine, Tulane University, New Orleans, Louisiana, United States.
David LeTulane University School of Medicine, Tulane University, New Orleans, Louisiana, United States.
Allison B McCoyDepartment of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States.

Funding

UAB Center for Clinical and Translational Sciences (CCTS)TL1TR001418 · NCATS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI SAAG, KENNETH G · 2015 to 2018
$2.1M
Electronic Health Record Safety: Developing Framework-Based Measures and Identifying Best PracticesR21LM012448 · NLM · VANDERBILT UNIVERSITY MEDICAL CENTER · PI MCCOY, ALLISON B · 2016 to 2017
$421k
NCATS NIH HHS TL1 TR001418NLM NIH HHS R21 LM012448
6 · The paper itself

Abstract

objectiveWe identified the methods used and determined the roles of electronic health records (EHRs) in detecting and assessing adverse drug events (ADEs) in the ambulatory setting.

methodsWe performed a systematic literature review by searching PubMed and Google Scholar for studies on ADEs detected in the ambulatory setting involving any EHR use published before June 2017. We extracted study characteristics from included studies related to ADE detection methods for analysis.

resultsWe identified 30 studies that evaluated ADEs in an ambulatory setting with an EHR. In 27 studies, EHRs were used only as the data source for ADE identification. In two studies, the EHR was used as both a data source and to deliver decision support to providers during order entry. In one study, the EHR was a source of data and generated patient safety reports that researchers used in the process of identifying ADEs. Methods of identification included manual chart review by trained nurses, pharmacists, and/or physicians; prescription review; computer monitors; electronic triggers; International Classification of Diseases codes; natural language processing of clinical notes; and patient phone calls and surveys. Seven studies provided examples of search phrases, laboratory values, and rules used to identify ADEs.

conclusionThe majority of studies examined used EHRs as sources of data for ADE detection. This retrospective approach is appropriate to measure incidence rates of ADEs but not adequate to detect preventable ADEs before patient harm occurs. New methods involving computer monitors and electronic triggers will enable researchers to catch preventable ADEs and take corrective action.

Indexed as

Electronic Health RecordsAmbulatory CareDrug-Related Side Effects and Adverse ReactionsHumans

Identifiers

PMID30786301
PMCPMC6382497

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.