Evidence map›Paper›PMID 38894513›Full record

ArticleAmerican journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists2024

Evaluation of an electronic health record Drug Interaction Customization Editor (DICE).

Andrew Romero, Ainhoa Gomez-Lumbreras, Lorenzo Villa-Zapata, Malinda Tan, John Horn, Daniel C Malone

Abstract readEvaluation Study
In one paragraph

Article in American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Andrew RomeroDepartment of Pharmacy, Tucson Medical Center, Tucson, AZ, USA.ORCID 0000-0002-5474-0579
Ainhoa Gomez-LumbrerasCollege of Pharmacy, University of Utah, Salt Lake City, UT.ORCID 0000-0002-3916-0402
Lorenzo Villa-ZapataDepartment of Clinical and Administrative Pharmacy, College of Pharmacy, University of Georgia, Athens, GA, USA.ORCID 0000-0002-3821-4595
Malinda TanCollege of Pharmacy, University of Utah, Salt Lake City, UT, USA.
John HornDepartment of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, USA.ORCID 0000-0003-4525-4250
Daniel C MaloneCollege of Pharmacy, University of Utah, Salt Lake City, UT, USA.ORCID 0000-0002-5006-9394

Funding

AHRQ HHS R01 HS025984
6 · The paper itself

Abstract

purposeDue to the low specificity of drug-drug interaction (DDI) warnings, hospitals and healthcare systems would benefit from the ability to customize alerts, thereby reducing the burden of alerts while simultaneously preventing harm. We developed a tool, called the Drug Interaction Customization Editor (DICE), as a prototype to identify features and functionality that could assist healthcare organizations in customizing DDI alerts.

methodsA team of pharmacists, physicians, and DDI experts identified attributes expected to be useful for filtering DDI warnings. A survey was sent to pharmacists with informatics responsibilities and other medication safety committee members to obtain their opinions about the tool. The survey asked participants to evaluate the 4 sections of the DICE tool (General, Medication, Patient, and Visit) on a scale ranging from 0 (not useful) to 100 (very useful). The survey provided an opportunity for participants to express their opinions on the overall usefulness of the DICE tool and to provide other comments.

resultsThe 50 survey respondents were mainly pharmacists (n = 47, 94%) with almost half (n = 23, 47%) having health information technology/informatics training. Most respondents (n = 33, 80%) were employed by organizations with over 350 beds. Respondents indicated the most useful features of the DICE tool were the ability to filter DDI warnings based on routes of administrations (mean [SD] rating scale score, 86.5 [21.6]), primary drug properties (85.7 [20.5]), patient attributes (85.6 [16.7]) and laboratory attributes (88.8 [18.0]). The overall impression of the DICE tool was rated at 82.8 (19.0), and when asked about the potential to reduce DDI alerts, respondents rated the tool at 83.7 (21.8).

conclusionThe ability to customize DDI alerts using data elements currently within the electronic health records (EHRs) has the potential to decrease alert fatigue and override rates. This prototype DICE tool could be used by end users and vendors as a template for developing a more advanced DDI filtering tool within EHR systems.

Indexed as

Drug InteractionsElectronic Health RecordsHumansMedical Order Entry SystemsMedication ErrorsPharmacistsSurveys and Questionnairesclinicaldecision support systemsdrug interactionselectronic health recordpharmacistsrisk management

Identifiers

PMID38894513
PMCPMC13368816

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.