Evidence map›Paper›PMID 31618781›Full record

ArticleApplied clinical informatics2019

Differences, Opportunities, and Strategies in Drug Alert Optimization-Experiences of Two Different Integrated Health Care Systems.

Salim M Saiyed, Katherine R Davis, David C Kaelber

Abstract readComparative Study
In one paragraph

Article 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 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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

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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.

Salim M SaiyedDivision of Health Sciences Informatics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States.
Katherine R DavisDepartment of Family Medicine, The MetroHealth System, Case Western Reserve University, Cleveland, Ohio, United States.
David C KaelberThe Center for Clinical Informatics Research and Education, The MetroHealth System, Case Western Reserve University, Cleveland, Ohio, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConcerns about the number of automated medication alerts issued within the electronic health record (EHR), and the subsequent potential for alarm fatigue, led us to examine strategies and methods to optimize the configuration of our drug alerts.

objectivesThis article reports on comprehensive drug alerting rates and develops strategies across two different health care systems to reduce the number of drug alerts.

methodsStandardized reports compared drug alert rates between the two systems, among 13 categories of drug alerts. Both health care systems made modifications to the out-of-box alerts available from their EHR and drug information vendors, focusing on system-wide approaches, when relevant, while performing more drug-specific changes when necessary.

resultsDrug alerting rates even after initial optimization were 38 alerts and 51 alerts per 100 drug orders, respectively. Eight principles were identified and developed to reflect the themes in the implementation and optimization of drug alerting.

conclusionA team-based, systematic approach to optimizing drug-alerting strategies can reduce the number of drug alerts, but alert rates still remain high. In addition to strategic principles, additional tactical guidelines and recommendations need to be developed to enhance out-of-the-box clinical decision support for drug alerts.

Indexed as

Delivery of Health Care, IntegratedMedical Order Entry SystemsElectronic Health RecordsMedication Errors

Identifiers

PMID31618781
PMCPMC6795529

What OpenQuestion holds

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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.