Evidence map›Paper›PMID 28339701›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2017

Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support.

Kin Wah Fung, Joan Kapusnik-Uner, Jean Cunningham, Stefanie Higby-Baker, Olivier Bodenreider

Abstract readComparative Study
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing 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

51 citing papers in PubMed.

  1. Article
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  4. Observational
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  18. Improving the specificity of drug-drug interaction alerts: Can it be done?American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2022
    Article
  19. Drug-Drug-Gene Interactions in Cardiovascular Medicine.Pharmacogenomics and personalized medicine · 2022
    Review
  20. 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

5 authors.

Kin Wah FungNational Library of Medicine, Bethesda, MD, USA.
Joan Kapusnik-UnerFirst Databank, South San Francisco, CA, USA.
Jean CunninghamTruven Health Analytics, Greenwood Village, CO, USA.
Stefanie Higby-BakerCerner Multum, Denver, CO, USA.
Olivier BodenreiderNational Library of Medicine, Bethesda, MD, USA.

Funding

Drug terminology at NLM - Integration, dissemination, quality assurance and applicationsZIALM010007 · NLM · NATIONAL LIBRARY OF MEDICINE · PI BODENREIDER, OLIVIER · 2016 to 2020
$3.1M
6 · The paper itself

Abstract

objectiveTo compare 3 commercial knowledge bases (KBs) used for detection and avoidance of potential drug-drug interactions (DDIs) in clinical practice.

methodsDrugs in the DDI tables from First DataBank (FDB), Micromedex, and Multum were mapped to RxNorm. The KBs were compared at the clinical drug, ingredient, and DDI rule levels. The KBs were evaluated against a reference list of highly significant DDIs from the Office of the National Coordinator for Health Information Technology (ONC). The KBs and the ONC list were applied to a prescription data set to simulate their use in clinical decision support.

resultsThe KBs contained 1.6 million (FDB), 4.5 million (Micromedex), and 4.8 million (Multum) clinical drug pairs. Altogether, there were 8.6 million unique pairs, of which 79% were found only in 1 KB and 5% in all 3 KBs. However, there was generally more agreement than disagreement in the severity rankings, especially in the contraindicated category. The KBs covered 99.8-99.9% of the alerts of the ONC list and would have generated 25 (FDB), 145 (Micromedex), and 84 (Multum) alerts per 1000 prescriptions.

conclusionThe commercial KBs differ considerably in size and quantity of alerts generated. There is less variability in severity ranking of DDIs than suggested by previous studies. All KBs provide very good coverage of the ONC list. More work is needed to standardize the editorial policies and evidence for inclusion of DDIs to reduce variation among knowledge sources and improve relevance. Some DDIs considered contraindicated in all 3 KBs might be possible candidates to add to the ONC list.

Indexed as

Decision Support Systems, ClinicalDrug InteractionsDrug Therapy, Computer-AssistedKnowledge BasesClinical Decision-MakingHumansMedical Order Entry Systemsclinical decision supportcommercial knowledge basecomputerized physician order entrydrug-drug interactionprescription decision support

Identifiers

PMID28339701
PMCPMC6080681

What OpenQuestion holds

Textmetadata
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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.