Evidence map›Paper›PMID 22539083›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA

High-priority drug-drug interactions for use in electronic health records.

Shobha Phansalkar, Amrita A Desai, Douglas Bell, Eileen Yoshida, John Doole, Melissa Czochanski, Blackford Middleton, David W Bates

Abstract readConsensus Statement
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing 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

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

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Review
  6. Article
  7. Review
  8. Evaluating NLP Approaches to Extract Drug Indications.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Toward a unified understanding of drug-drug interactions: mapping Japanese drug codes to RxNorm concepts.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. 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

18 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

8 authors.

Shobha PhansalkarDivision of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA. sphansalkar@partners.org
Amrita A Desai
Douglas Bell
Eileen Yoshida
John Doole
Melissa Czochanski
Blackford Middleton
David W Bates

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop a set of high-severity, clinically significant drug-drug interactions (DDIs) for use in electronic health records (EHRs).

methodsA panel of experts was convened with the goal of identifying critical DDIs that should be used for generating medication-related decision support alerts in all EHRs. Panelists included medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use. Candidate DDIs were assessed by the panel based on the consequence of the interaction, severity levels assigned to them across various medication knowledge bases, availability of therapeutic alternatives, monitoring/management options, predisposing factors, and the probability of the interaction based on the strength of evidence available in the literature.

resultsOf 31 DDIs considered to be high risk, the panel approved a final list of 15 interactions. Panelists agreed that this list represented drugs that are contraindicated for concurrent use, though it does not necessarily represent a complete list of all such interacting drug pairs. For other drug interactions, severity may depend on additional factors, such as patient conditions or timing of co-administration. DISCUSSION: The panel provided recommendations on the creation, maintenance, and implementation of a central repository of high severity interactions.

conclusionsA set of highly clinically significant drug-drug interactions was identified, for which warnings should be generated in all EHRs. The panel highlighted the complexity of issues surrounding development and implementation of such a list.

Indexed as

Adverse Drug Reaction Reporting SystemsDecision Support Systems, ClinicalDrug InteractionsDrug Therapy, Computer-AssistedElectronic Health RecordsMedical Order Entry SystemsMedication SystemsHumans

Identifiers

PMID22539083
PMCPMC3422823

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.