ArticleJournal of medical Internet research2021
Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials.
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.
What it found
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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.
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.
Optimizing the Influenza Clinical Decision Support Tool
Using Rapid Cycle Randomized Controlled Trials to Study a Tobacco Cessation Best Practice Advisory.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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 · 2026Pooled it
- The Influence of Artificial Intelligence Scribes on Clinician Experience and Efficiency among Pediatric Subspecialists: A Rapid, Randomized Quality Improvement Trial.Applied clinical informatics · 2025Trial
- AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.Nature medicine · 2024Trial
- Digital solutions in acute medicine: Will electronic health records join up the patient journey (soon?).Future healthcare journal · 2026Article
- Clinical artificial intelligence applications of vision-language foundation models.PLOS digital health · 2026Review
- A data and knowledge cross-level fusion-driven learning framework for detecting missing diagnosis.NPJ digital medicine · 2026Article
- Interactive, Personalized Patient Decision Aid for COVID-19 Vaccination in Canada: User-Centered Design Approach.JMIR human factors · 2026Article
- Decoding Clinician Authorial Style: A Style-Informed Pipeline for Clinical Document Summary Generation with Large Language Models.Research square · 2026Article
- Empowering AI assisted clinical drug development: tactics to address data bias, the digital divide and missing patient populations through AI and digital solutions.Frontiers in digital health · 2026Review
- Data-Driven Implementation Trials: Realizing Their Full Potential in Achieving the Promise of Learning Health Systems.Learning health systems · 2026Article
- Generative artificial intelligence in medicine.Nature medicine · 2025Review
- Learning health system strategies in the AI era.npj health systems · 2025Review
- Agile evaluation including two pragmatic trials on the uptake of a digital screening service.NPJ digital medicine · 2025Article
- 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 · 2025Article
- The Elements of Style for Interruptive Electronic Health Record Alerts.Applied clinical informatics · 2025Article
- Methodological Challenges in Randomized Controlled Trials of mHealth Interventions: Cross-Sectional Survey Study and Consensus-Based Recommendations.Journal of medical Internet research · 2024Article
- Impact of an Electronic Health Record-Based Interruptive Alert Among Patients With Headaches Seen in Primary Care: Cluster Randomized Controlled Trial.JMIR medical informatics · 2024Article
- Social Media's Lessons for Clinical Decision Support: Strategies to Improve Engagement and Acceptance.Applied clinical informatics · 2024Article
- The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring.Journal of medical Internet research · 2024Article
- Efficacy of Digital Outreach Strategies for Collecting Smoking Data: Pragmatic Randomized Trial.JMIR formative research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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What OpenQuestion holds
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.