Evidence map›Paper›PMID 33835035›Full record

ArticleJournal of medical Internet research2021

Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials.

Jonathan Austrian, Felicia Mendoza, Adam Szerencsy, Lucille Fenelon, Leora I Horwitz, Simon Jones, Masha Kuznetsova, Devin M Mann

2 registry-linked trialsAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing 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.

NCT03415425 nacompletednot on this map

Optimizing the Influenza Clinical Decision Support Tool

TypeinterventionalSponsorNYU Langone HealthRan2018 to 2023Enrolled25,881ConditionsClinical Decision SupportArmsCurrent Version of the Influenza Alert, Alternate Modifications of the Influenza Alert
NCT03714191 completednot on this map

Using Rapid Cycle Randomized Controlled Trials to Study a Tobacco Cessation Best Practice Advisory.

TypeobservationalSponsorNYU Langone HealthRan2018 to 2020Enrolled54,417ConditionsTobacco Use CessationArmsPatient Outcomes, Billing documentation, Regulatory
3 · Its place in the literature

Who cites it

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

  1. Factors associated with response to patient-reported outcome measures: a systematic review of systematic and scoping reviews, and meta-analyses.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
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  14. Opening the contextual black box: a case for idiographic experience sampling of context for clinical applications.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
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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

8 authors.

Jonathan AustrianDepartment of Medicine, NYU Grossman School of Medicine, New York, NY, United States.ORCID 0000-0002-0016-4705
Felicia MendozaDepartment of Population Health, NYU Grossman School of Medicine, New York, NY, United States.ORCID 0000-0002-8920-2105
Adam SzerencsyDepartment of Medicine, NYU Grossman School of Medicine, New York, NY, United States.ORCID 0000-0002-0066-4400
Lucille FenelonMedical Center Information Technology, NYU Langone Health, New York, NY, United States.ORCID 0000-0002-5046-0801
Leora I HorwitzDepartment of Medicine, NYU Grossman School of Medicine, New York, NY, United States.ORCID 0000-0003-1800-6040
Simon JonesDepartment of Population Health, NYU Grossman School of Medicine, New York, NY, United States.ORCID 0000-0002-8267-5647
Masha KuznetsovaDepartment of Population Health, NYU Grossman School of Medicine, New York, NY, United States.ORCID 0000-0001-6710-526X
Devin M MannDepartment of Medicine, NYU Grossman School of Medicine, New York, NY, United States.ORCID 0000-0002-2099-0852

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools.

objectiveThis paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care.

methodsA multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior.

resultsTo demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images.

conclusionsThese experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS.

trial registrationFlu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191.

Indexed as

Decision Support Systems, ClinicalElectronic Health RecordsHumansRandomized Controlled Trials as TopicSoftwareAB testingalert fatigueclinical decision supportclinical informaticsrandomized controlled trialsusability

Identifiers

PMID33835035
PMCPMC8065554

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.